{
"cells": [
{
"cell_type": "markdown",
"id": "8fd9c005-a2a8-42d8-b183-7dfba05ea20d",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### Project Approval(s): {-}\n",
"\n",
"Iyad Obeid, PhD., Project Advisor, Electrical and Computer Engineering (Temple University)\n",
"\n",
"Joseph Picone, PhD., Electrical and Computer Engineering (Temple University)"
]
},
{
"cell_type": "markdown",
"id": "e1b75b21",
"metadata": {},
"source": [
"## Libraries and Defines {-}\n",
"Include the dependent libraries for this notebook and define variables that will be used throughout the notebook:"
]
},
{
"cell_type": "code",
"execution_count": 618,
"id": "e618b613",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import time\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import datetime as dt\n",
"from importlib import reload\n",
"\n",
"pd.options.mode.chained_assignment = None # default='warn'"
]
},
{
"cell_type": "markdown",
"id": "099ccd43",
"metadata": {},
"source": [
"### Import/Re-Import Custom Makin Library {-}\n",
"The custom python library developed for this project is *makin_2018_tools.py*. This is a developing library, so it might change while working in this notebook. If code is changed, the code below will re-import the library with its latest changes."
]
},
{
"cell_type": "code",
"execution_count": 247,
"id": "54f513c5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Makin Library was imported with file last modified at: 04/16/2024 06:14:49\n"
]
}
],
"source": [
"# will be working on makin_tools while developing this notebook\n",
"# so if it has changed, reload it\n",
"\n",
"\n",
"# check the latest modification time of the custom Makin Lib\n",
"makinLibMod_t1 = os.path.getmtime('../makin_2018_tools.py')\n",
"\n",
"# was the time 2 variable created (which would happen when loading the lib)\n",
"if 'makinLibMod_t2' in globals():\n",
" if (makinLibMod_t1 != makinLibMod_t2):\n",
" print(\"Reloading makin_2018_tools.py\")\n",
" mt = reload(mt)\n",
"else:\n",
" ## make sure to add the parent directory to the python search path\n",
" ## that is where the makin_tools lib is\n",
" sys.path.append(\"../\")\n",
" import makin_2018_tools as mt\n",
" \n",
"makinLibMod_t2 = os.path.getmtime('../makin_2018_tools.py')\n",
"makinLibMod_T2_str = time.strftime('%m/%d/%Y %H:%M:%S',\\\n",
" time.gmtime(makinLibMod_t2))\n",
"\n",
"print(f\"The Makin Library was imported with file last modified at:\\\n",
" {makinLibMod_T2_str}\")"
]
},
{
"cell_type": "markdown",
"id": "5fcfc049-357e-4f8c-a396-89fb38f01fb7",
"metadata": {},
"source": [
"# Abstract\n",
"\n",
"The purpose for this project is to write, document, and publish a Python library for neural signal decoding to use in conjunction with a published dataset ([O'Doherty et al., 2020](https://zenodo.org/records/3854034)). Specifically, this library is built to match results and implement decoders seen in the results file accompanying the O’Doherty dataset and collected by [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95). This Python library is publicly available at [Samarco and Obeid 2024](https://github.com/Neural-Instrumentation-Lab/makin_2018_reproduction). The fundamental neural decoding applied here is prediction of fingertip kinematics (that is, position, velocity, and acceleration in two dimensions) from the firing rates of populations of single-unit (SU) neurons observed in small temporal windows (or bins). The results reported by [O'Doherty et al., 2020](https://zenodo.org/records/3854034) include 7 different decoders (linear regression, Kalman Filter (KF) supervised, KF unsupervised with static mapping, KF unsupervised with a dynamic (KF) mapping, unscented KF (UKF), recurrent exponential-family harmonium (rEFH) with static mapping, and rEFH with dynamic (KF) mapping) for *47* reaching trials for *2* different monkeys (“indy” and “loco”). In this work, 3 of those algorithms (linear regression, KF supervised, and KF unsupervised with static mapping) have been implemented in Python and documented sufficiently such that other investigators can easily modify or extend their functionality. Results were collected for these implementations on the [O'Doherty et al., 2020](https://zenodo.org/records/3854034) data and compared for exactness to [Makin et al., 2018's](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) MATLAB based solution. Of the *2* monkeys for the dataset, this effort was not able to effectively reconstruct the “loco” monkey results (i.e. $21$ % of the results) and so reported results in this project are made in reference to only the “indy” monkey. The average difference in signal-to-noise ratio (SNR) performance from the [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) results, when considering all kinematic states and tested bin widths ($16\\text{ ms}$, $32\\text{ ms}$, $64\\text{ ms}$, and $128\\text{ ms}$), was at most ~$2$ % (with $\\lt1$ % difference in standard error) for linear regression and KF supervised. The KF unsupervised with static mapping decoder implemented in this work demonstrated an improvement over [Makin et al., 2018's](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) implementation with the average improvement in SNR performance being $\\gt 59$ % ($p \\gt 0.05$; when considering all kinematic states and tested bin widths).\n",
"\n",
"The library implemented here was further extended to allow neuron dropping and spike pooling on observational data to test decoding results under sub-optimal decoding conditions. Using spike pooling, or multi-unit (MU), neural observations in place of SU observations proved beneficial or did not substantially affect acceleration estimation for the regression and KF supervised decoders when binning at $128\\text{ ms}$ (percent increase from average SU SNR of $82$ % and $27$ % for acceleration in the $x$ and $y$ direction respectively for regression ($p \\gt 0.15$); percent decrease from average SU SNR of $\\lt0.5$ % and $\\lt4$ % for acceleration in the $x$ and $y$ direction respectively for KF supervised ($p \\gt 0.15$)). In the case of randomly dropping spikes, uniformly, from single-unit neurons, linear regression degrades in an approximate linear fashion with the percentage of dropped spikes when considering all kinematic state results combined for any bin size (e.g., $5$ % of randomly dropped spikes corresponds to a $5$ % decrease in SNR). In considering the KF supervised decoder, a decrease in SNR performance of $\\lt9$ % can be expected when considering all kinematic results overall and when randomly dropping up to $15$ % of all spikes recorded. For the KF unsupervised with static mapping decoder, removing a random $5$ % of spikes essentially produced no change in SNR performance when considering all kinematic states results for any bin size ($p \\gt 0.05$)."
]
},
{
"cell_type": "markdown",
"id": "b3990b08-314f-4241-b61c-01e1829151fd",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"## Motivation\n",
"\n",
"Brain Machine Interface (BMI) decoders bridge the connection between electrical signals of the brain, or observed neuron activity, and typically some high-level task or thought process carried out by a human or other animal. These decoders can involve decoding of signals down to the single neuron level for real-time interpretation of a desired thought or task. Therefore, BMI decoders are vital in the development of prosthetics, as well as other forms of brain-machine control. Many research efforts today are focused on decoding brain activity to interpret tasks intended to be carried out by the body’s motor system. One group previously used finger-tip kinematic decoding in a BMI system involving real non-human primate experiments and a published dataset ([Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95); [O'Doherty et al., 2020](https://zenodo.org/records/3854034)). [Makin et al., 2018's](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) made a direct comparison, measuring signal-to-noise ratio (SNR), among early, conventional, and contemporary BMI decoders like Kalman filters (supervised, unsupervised, unscented, etc.) and the newly introduced BMI decoder—the recurrent Exponential-Family Harmonium (rEFH). The decoders were implemented in MATLAB (code can be found at [Makin and O’Doherty 2018](https://github.com/jgmakin/rbmish)) and the results for each decoder's performance for each experimental data file was published alongside the dataset (as a *.csv* file). The MATLAB code by [Makin and O’Doherty 2018](https://github.com/jgmakin/rbmish) was not written in a way to directly reproduce the results that accompany the [O'Doherty et al., 2020](https://zenodo.org/records/3854034) published neural-kinematic signal dataset. It was specifically published with instructions on how to use their code to run a general exponential-family harmonium, or EFH. It does contain a \"neural analysis\" folder, which looks to house functions that might ultimately be pieced together to replicate the results published with the [O'Doherty et al., 2020](https://zenodo.org/records/3854034) dataset.\n",
"\n",
"This project's aim is to develop a Python library for neural signal decoding that is well documented and can be directly implemented on a published dataset. The [O'Doherty et al., 2020](https://zenodo.org/records/3854034) dataset was chosen as the dataset to use for this library for several reasons. The primary reason being that it has results published with it for many relevant and new decoders, which were collected in a publication ([Makin and O’Doherty 2018](https://github.com/jgmakin/rbmish)). The results effectively serve to prove the decoders implementation here as correct. Another reason is that the dataset contains data over a span of *44* different days and over a collection for two different subjects, or monkeys (named \"loco\" and \"indy\"). This can be advantageous when considering investigating the concept of transfer learning, or how decoder model information can be leveraged from subject to subject or from one day to another.\n",
"\n",
"After successfully implementing the decoders, an additional sub-task will be pursued—that is, quantifying the implemented neural-kinematic signal decoder's performance (SNR) when presented with sub-optimal data. The results file that this library uses to confirm decoder implementation, reflects results for \"single-unit\" (SU) neuron spike data. This project will explore the performance expected for these decoders on this dataset when the \"spike-sorting\" process is skipped and the original SU detected neurons are all pooled in the electrode channel that they were sorted on. Spike-sorting typically entails a complex and computationally expensive process to associate single neurons to recorded neural “spikes” and thus is not always feasible for every application. Lastly, in practical BMI systems, especially wireless systems, observational data is subject to dropping out. This project will also quantify the performance (SNR) among the implemented decoders when single-unit neural data is randomly dropped over a session for different drop rates."
]
},
{
"cell_type": "markdown",
"id": "91f66ecf-b3b9-4571-9243-cdc899fe7a97",
"metadata": {},
"source": [
"## Background\n",
"\n",
"### Data Acquisition Overview\n",
"\n",
"![BMI System Overview](./images/bmi_system_overview.png)\n",
"\n",
"*Figure 1. This is an overview of a typical brain machine interface system. (a) The subject has chronically implanted electrodes in its brain to record neural activity. The subject can be a person or non-human primate. (b) An example photo of micro-electrode arrays implanted in brain tissue (photo from [Rajan et al., 2015](https://pubmed.ncbi.nlm.nih.gov/26479701/)). Specifically, this image shows Utah arrays ([Blackrock Neurotech (New York City, New York)](https://blackrockneurotech.com/products/utah-array/#:~:text=What%20is%20the%20Utah%20Array,degree%20of%20precision%20and%20accuracy.)). (c) Each electrode measures raw electrical potential from the neurons that neighbor it. (d) A pre-amplifier, filters, amplifies, and samples, or digitizes, the raw analog electrode signals. This conditions the signal for the processing system. (e) The processing system processes the digitized electrode signals and runs application algorithms on the data to achieve a task. (f) The algorithms typically consist of a spike detection phase, followed by (or optionally skipped) a spike sorting/classification algorithm. The “spike” data is asynchronous and usually needs to be time aligned, or binned. The binned spike data then feeds the main application algorithm/model which essentially decodes that data and transforms it to an equivalent action, task, and/or state of a system. (g) Finally, the prediction from the decoder algorithm can be used to update the state of a device (for example, control position of a prosthesis).*\n",
" \n",
" \n",
"Figure 1 above depicts a typical overview and flow for acquired neural data in a single-unit BMI system. This project is mostly concerned with the decoding aspect, or the (f) block seen in figure 1.\n",
"\n",
"In single-unit BMI collection systems, micro-electrode arrays are chronically implanted extracellular to neural tissue. The electrodes, forming the array, sense the electrical signals generated by neurons in their proximity. Each electrode will produce a raw signal comprised of the superposition of potentials sensed from neurons closest to it. These electrodes produce very small (typically in the range of 100s of microvolts), noisy analog signals.\n",
"\n",
"The electrode signals are typically passed to a pre-amplifier prior to processing the signals on a digital platform. The pre-amplifier attenuates unwanted noise via filtering, amplifies the signals, and then converts these analog signals into digital signals with quantized amplitudes and periodic sampling. Acommonl BMI pr-eamplifier is the[ PZ2 Pr-eamplifier (Tucker-Davis Technologies, Alachua, FL](https://www.tdt.com/docs/hardware/preamplifiers/)). The digitized electrode recordings can be processed raw (for example, potential vs. time) or “spike detected” to detect neuron action potentials. A leading theory of neural function is that information is encoded in neural spike timing.\n",
"\n",
"To extract neural spike times, a processor system will typically be equipped with some spike detection, sorting, and binning algorithms. Spikes can be detected using static or adaptive simple thresholds, or with more sophisticated tools such as Wiener Filters. Following spike detection is spike sorting which classifies the detected spikes, sorting them to individual neurons. While spike sorting is technically an optional process, it is generally accepted as an important step. At this point, the “spikes” are reduced to merely “firing” times, or the time that the neuron spiked/activated. Since a spike can happen at any given time, these firing times are asynchronous and do not align with any periodic sampling rate. Therefore, to synchronize the firing times to a sampling period, the firing times are “binned” to produce spike counts at each periodic sampling interval.\n",
"\n",
"The spike counts on each sampling interval, for each neuron or electrode (in the case of skipping spike sorting) is passed to a decoder, which maps those spike counts to an equivalent state of a system. This allows for the potential to update a system to that predicted state.\n",
"\n",
"Finally, it is worth noting that the pipeline for the neural data flow can be wired or wireless at any point after electrode measurement at the source (the brain). Wireless transmission can present some challenges that might need to be considered such as data dropouts."
]
},
{
"cell_type": "markdown",
"id": "0c1a11d3-33c8-4f8d-8526-a71fe93da511",
"metadata": {},
"source": [
"### Spike Sorted or Single-Unit Neural Data\n",
"\n",
"Spike “sorting” is a method aimed at differentiating between multiple single neurons detected on the same electrode. Conventionally, spike sorting entails a three-step process ([Zhang et al., 2023](https://pubmed.ncbi.nlm.nih.gov/36972585/)). First, a spike detection algorithm reduces the electrode data from all time samples to just segments, or periods, where the electrodes are thought to have recorded a neuron firing/producing an action potential. Then, a feature extraction algorithm is deployed to discover features that best explain the differences among the different neurons. Finally, a classification algorithm is applied to the features and labels are placed for most likely fit of which neuron produced which “spike”. Spike sorting is computationally expensive but provides finest grain detail on neuronal function.\n",
"\n",
"As [Zhang et al., 2023](https://pubmed.ncbi.nlm.nih.gov/36972585/) has illustrated (see Figure 1 in that paper), spike sorting is gaining traction as a fundamental process to BMI systems with the number of spike sorting publications increasing exponentially since the 1950's. However, spike sorting also has its drawbacks. Even putting aside the added computational complexity, spike sorting is an added process that typically requires rounds of training for development of classification/clustering models. Furthermore, as [Zhang et al., 2023](https://pubmed.ncbi.nlm.nih.gov/36972585/) points out in some of their descriptions for the various spike sorting algorithms (for example, K-means, Spiking Neural Networks, Template Matching, etc.), this development can require manual calibration. This limits actual time to application (for example, controlling a prosthesis)."
]
},
{
"cell_type": "markdown",
"id": "9f1c48e6-4b72-441c-84ed-503fe7be0bb9",
"metadata": {},
"source": [
"### Pooled or Multi-Unit Neural Data\n",
"\n",
"An alternative approach to spike sorting is using multi-unit spike detected data. In multi-unit (MU) data, each electrode is essentially treated as a single neuron with all detected spikes lumped together into a single binned dimension in the neural measurement that feeds the decoder, $\\pmb{r}_m$. There has been recent work aimed at skipping the conventional spike sorting process and in testing the feasibility of multi-unit data decoding performance (for example, [Chestek et al., 2011](https://pubmed.ncbi.nlm.nih.gov/21775782/), [Todorova et al., 2014](https://pubmed.ncbi.nlm.nih.gov/25082508/#:~:text=Decoding%20based%20on%20spike%2Dsorted,and%20simple%20method%20is%20competitive.), and [Trautmann et al., 2019](https://pubmed.ncbi.nlm.nih.gov/31171448/)). [Trautmann et al., 2019](https://pubmed.ncbi.nlm.nih.gov/31171448/) reproduced the results from three separate spike sorting publications, but instead of spike sorting, used multi-unit data for decoding and demonstrated that the results were very similar to the original spike sorted case. Their conclusion was that multi-unit data can be especially effective when decoding activity is reliant on population neural data as opposed to single neurons.\n",
"\n",
"Spike sorting adds an additional layer of complexity to the BMI chain, which can make multi-unit more favorable to some applications. This complexity will also scale with the number of electrode channels, which can be an issue with spike sorting as the number of electrodes employed in recent BMI studies are reaching the thousand ([Musk and Neuralink, 2019](https://www.jmir.org/2019/10/e16194/); [Steinmetz, 2020](https://pubmed.ncbi.nlm.nih.gov/31951220/)). Added complexity comes with the demand for more powerful computational resources, which comes with added size, power, and thermal requirements. For embedded/real-time applications, this may prove non-feasible based on the inherent biological requirements at hand—again, making a case for multi-uni processing. Hence, as mentioned previously as a sub-goal, decoders implemented in this project will be evaluated for their handling of MU observational data."
]
},
{
"cell_type": "markdown",
"id": "d209c1d6-b7ab-431c-a81e-391d46c43420",
"metadata": {},
"source": [
"### Neural Decoding\n",
"\n",
"Extensive research in the BMI field is aimed at decoding neural activity with the intent to translate that into or predict a certain action or task performed by an animal. “Decoding” is the process of deciphering what the neurons are ‘thinking’ about with respect to a particular task. It is common to see a research effort directed at decoding an action involving an arm reach, finger movement, or some other bodily kinematic state. This type of research is vital to the development of BMI systems targeted for prosthesis. Specifically, in this effort, the decoding will involve the trajectory (or the position, velocity, and acceleration) of a fingertip from a monkey performing reaches to targets in space.\n",
"\n",
"In the past, researchers employed linear models trained with regression to do this decoding. Later models used more sophisticated probabilistic linear filters—primarily a variant of the Kalman filter—to do this. Contemporary approaches allow for the neural-kinematic model to have non-linearities, be non-Gaussian, and for the training be unsupervised. Specifically, one of the more recent filters introduced into the BMI field for this contemporary style of modeling is the recurrent exponential-family harmonium (rEFH). In 2018, a research paper was published that introduced the rEFH used in this regard and compared performance among the different existing type of filters/modeling methods mentioned here ([Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95)). For the Python library being built in this work, decoders seen in the aforementioned\r\n",
" paper will be implemented here and results will be compared on the same dataset to confirm the implementation."
]
},
{
"cell_type": "markdown",
"id": "d24b1f0a-213e-47dc-8782-b0ef5322797c",
"metadata": {},
"source": [
"## Dataset\n",
"**The dataset for this project can be found here**:\n",
"\n",
"*O’Doherty, J. E., Cardoso, M. M. B., Makin, J. G., & Sabes, P. N. (2020, May 26).*\n",
"\n",
"*Nonhuman primate reaching with Multichannel Sensorimotor Cortex Electrophysiology.*\n",
"\n",
"*Zenodo. https://zenodo.org/doi/10.5281/zenodo.788569*\n",
" \n",
"\n",
"### Experiment\n",
"The data collected is from a series of repeated experiments on a couple monkeys, who had micro-electrode arrays implanted into their brains and a BMI system set up to record their single-unit neural spikes and ground truth kinematic fingertip data. In the experiments the monkeys made reaches in space to hit targets (target positions were recorded in real time). The reaches were performed in the zone just below shoulder level and the only kinematic states recorded were position in the \"x\" and \"y\" direction. The x and y plane are defined as shown in the illustration below for a subject (Figure 2).\n",
"\n",
"
\n",
"\n",
"\n",
"*Figure 2. The axes defined for fingertip kinematics from [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95). Reaching tasks were performed in the x-y plane to hit targets with a fingertip. +x corresponded to reaches to the right of the subject and +y corresponded to reaches rostral to the subject (\"-\" is opposite that).*\n",
"\n",
"\n",
"\n",
"### Session/File Information:\n",
"Each data file from the *Nonhuman primate reaching with Multichannel Sensorimotor Cortex Electrophysiology* experiment contains data collected from a \"session\" of the experiment. The data file is named with respect to the name of the subject, or monkey, and session date and number for which the data was collected on (e.g. file *indy_20160411_02.mat* has data collected from the 2nd session on 04/11/2016 and from a monkey named \"Indy\"). The data files are formatted as \".mat\" files (i.e. binary MATLAB files, which store MATLAB workspace variables).\n",
"\n",
"In the following code below, all of the data file/session names, as well as their complete paths, will be collected. The number of sessions is counted and some general information, provided in the file names, is obtained as well."
]
},
{
"cell_type": "code",
"execution_count": 250,
"id": "74da537f-04e1-447a-b181-608de10532b0",
"metadata": {},
"outputs": [],
"source": [
"# 0. ******* First, define the path where the data files are stored\n",
"# (this is hard-coded and provided by reader/user):\n",
"fdir_dat = r\"/data/isip/data/makin_primate\""
]
},
{
"cell_type": "code",
"execution_count": 251,
"id": "487e2c87-a857-4751-b695-4ccc9bb00bcf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files in Dataset:\n",
"indy_20160407_02.mat\n",
"indy_20160411_01.mat\n",
"indy_20160411_02.mat\n",
"indy_20160418_01.mat\n",
"indy_20160419_01.mat\n",
"indy_20160420_01.mat\n",
"indy_20160426_01.mat\n",
"indy_20160622_01.mat\n",
"indy_20160624_03.mat\n",
"indy_20160627_01.mat\n",
"indy_20160630_01.mat\n",
"indy_20160915_01.mat\n",
"indy_20160916_01.mat\n",
"indy_20160921_01.mat\n",
"indy_20160927_04.mat\n",
"indy_20160927_06.mat\n",
"indy_20160930_02.mat\n",
"indy_20160930_05.mat\n",
"indy_20161005_06.mat\n",
"indy_20161006_02.mat\n",
"indy_20161007_02.mat\n",
"indy_20161011_03.mat\n",
"indy_20161013_03.mat\n",
"indy_20161014_04.mat\n",
"indy_20161017_02.mat\n",
"indy_20161024_03.mat\n",
"indy_20161025_04.mat\n",
"indy_20161026_03.mat\n",
"indy_20161027_03.mat\n",
"indy_20161206_02.mat\n",
"indy_20161207_02.mat\n",
"indy_20161212_02.mat\n",
"indy_20161220_02.mat\n",
"indy_20170123_02.mat\n",
"indy_20170124_01.mat\n",
"indy_20170127_03.mat\n",
"indy_20170131_02.mat\n",
"loco_20170210_03.mat\n",
"loco_20170213_02.mat\n",
"loco_20170214_02.mat\n",
"loco_20170215_02.mat\n",
"loco_20170216_02.mat\n",
"loco_20170217_02.mat\n",
"loco_20170227_04.mat\n",
"loco_20170228_02.mat\n",
"loco_20170301_05.mat\n",
"loco_20170302_02.mat\n"
]
}
],
"source": [
"# Load All of the data files, or sessions, from the dataset\n",
"\n",
"# 1. ******* Collect all the datafiles and information regarding sessions\n",
"fnames = [] # array for data file names\n",
"monkeys= [] # array for names of monkeys from the experiments\n",
"dates = [] # array for experiment dates\n",
"\n",
"for file in os.listdir(fdir_dat):\n",
" # files that end in \".mat\" are the data files\n",
" if file.endswith(\".mat\"):\n",
" # collect filename\n",
" fnames.append(file)\n",
" \n",
" # get monkey name\n",
" monkey = file.split(\"_\")[0]\n",
" if (monkey not in monkeys):\n",
" monkeys.append(monkey)\n",
" \n",
" # get dates\n",
" date_str = file.split(\"_\")[1]\n",
" date_num = dt.datetime.strptime(date_str, '%Y%m%d')\n",
" if (date_str not in dates):\n",
" dates.append(date_str) # collect new date\n",
" \n",
" # update earliest and latest date\n",
" if (len(dates) > 1):\n",
" if ((date_num - date_min).days < 0):\n",
" date_min = date_num\n",
" \n",
" if ((date_num - date_max).days > 0):\n",
" date_max = date_num\n",
" else:\n",
" date_min = date_num\n",
" date_max = date_num\n",
"\n",
" \n",
"# sort the files alpha-numerically\n",
"# (easier to compare to provided results excel file)\n",
"fnames = sorted(fnames)\n",
"\n",
"# 2. ******* Print names of files in dataset:\n",
"print(\"Files in Dataset:\")\n",
"for i in fnames:\n",
" print(i)"
]
},
{
"cell_type": "markdown",
"id": "e24f9461-ed96-4f81-839f-ce2e914ed282",
"metadata": {},
"source": [
"From the data files, the following information is gathered:"
]
},
{
"cell_type": "code",
"execution_count": 252,
"id": "785d0c00-8968-45df-bea2-0835bbaa1f72",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of files in dataset:\t\t\t\t 47\n",
"Number of monkey subjects from experiment:\t\t 2 (Names: ['indy', 'loco'])\n",
"Number of days sessions were had:\t\t\t 44 days\n",
"Span of days from first session collect to last:\t 329 days (2016-04-07, 2017-03-02)\n"
]
}
],
"source": [
"# print number of files, monkeys, session days, and day span for sessions\n",
"\n",
"print(f\"Number of files in dataset:\\t\\t\\t\\t {len(fnames):3d}\")\n",
"print(f\"Number of monkey subjects from experiment:\\t\\t\" +\\\n",
" f\" {len(monkeys):3d} (Names: {monkeys})\")\n",
"print(f\"Number of days sessions were had:\\t\\t\\t {len(dates):3d} days\")\n",
"print(f\"Span of days from first session collect to last:\\t\" +\\\n",
" f\" {(date_max - date_min).days:3d} days\" +\\\n",
" f\" ({date_min.strftime('%Y-%m-%d')}, \" +\\\n",
" f\"{date_max.strftime('%Y-%m-%d')})\")"
]
},
{
"cell_type": "markdown",
"id": "b93984c6-5489-4241-891c-6904f29eb788",
"metadata": {},
"source": [
"#### Session Variables:\n",
"Now, to take a more in-depth look into the contents of these files, the custom *makin_2018_tools.py* library will be employed. The code below loads the variables from the given data file into the workspace (object variable, *data*). It also renames the original variable \"spikes\" from the \".mat\" file to \"Espks\" and adds variables \"Sspks\", \"Mspks\" and \"Elabels.\" The variable \"Espks\" is an array of arrays. The first dimension of \"Espks\" represents electrode data. Within each element of the first dimension are arrays of data for each of the detected single-units from that electrode. Each single-unit array contains the times when that single-unit neuron spiked.\n",
"\n",
"The \"Sspks\" and \"Mspks\" variables are created from the \"Espks\" variable. \"Sspks\" is the \"valid\" unpacked electrode data, or the collection of single-unit spike time arrays for all of the \"valid\" neurons. A \"valid\" neuron is one which exhibits a firing rate greater than or equal to 0.5 Hz. \"Mspks\" is the unpacked single-unit neuron data per electrode (spike sorting to individual neurons is removed here). Lastly, the \"Elabels\" array is an array of labels which track the available electrodes from all original channels. Note that some electrodes may be unavailable (have no data on them) for a session or could have been dropped in the case of no \"valid\" neuron being detected on it."
]
},
{
"cell_type": "code",
"execution_count": 253,
"id": "9094f0ca-4a00-4b58-bb4a-3da33ead7961",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable | Size of Variable\n",
"chan_names | 192 \n",
"cursor_pos | (204446, 2) \n",
"finger_pos | (204446, 3) \n",
"t | (204446,) \n",
"target_pos | (204446, 2) \n",
"wf | 192 \n",
"Espks | 192 \n",
"Elabels | 141 \n",
"Mspks | 141 \n",
"Sspks | 291 \n"
]
}
],
"source": [
"# provide complete data path for sample data file\n",
"fpath = fdir_dat + r\"/\" + fnames[0]\n",
"\n",
"# extract data from the test file\n",
"data = mt.load_data(fpath)\n",
"\n",
"# extract variables from the data dictionary\n",
"## define print template and headers\n",
"printTemp = \"{0:15} | {1:15}\"\n",
"print(printTemp.format(\"Variable\", \"Size of Variable\"))\n",
"for key,val in data.items():\n",
" try:\n",
" print(printTemp.format(f\"{key}\",f\"{val.shape}\"))\n",
" except: \n",
" print(printTemp.format(f\"{key}\",f\"{len(val)}\"))\n",
" exec(key + '=val')"
]
},
{
"cell_type": "markdown",
"id": "26e2e05d-5883-4160-bb5c-4ae94b252f34",
"metadata": {},
"source": [
"The variables from the \"data\" dictionary above can be defined in the following way:\n",
"\n",
"* **chan_names**: This variable is an array that contains the name of each electrode. The electrodes are named by the region of the brain they were placed in (i.e. primary somatosensory (S1) or primary motor (M1) cortex) and their number in the electrode array in that region of the brain. This is size $E$, with $E$ being the total number of electrodes.\n",
" \n",
" \n",
"* **cursor_pos**: This variable contains the computer cursor that tracked the finger tip location during the reaches in the experiment. This is an array of $x$ and $y$ position (cartesian coordinates) and in units of mm. This is size $(M,2)$ with 2 being the number of positional coordinates and $M$ being the total number of time samples collected.\n",
" \n",
" \n",
"* **finger_pos**: This variable contains the positional measurements of the monkey's finger tip in cartesian coordinates (z,-x,-y) in units of cm. This is size $(M,3)$ with 3 being the number of positional coordinates.\n",
" \n",
" \n",
"* **t**: This is the time vector for the session. This vector contains the times when the synchronously sampled data (e.g. \"cursor_pos,\" \"finger_pos,\" \"target_pos\") were measured in the session. This time vector is in seconds and represents elapsed time in the session (vector of length $M$).\n",
" \n",
" \n",
"* **target_pos**: This is the position for each target, which the monkeys are tasked with making finger tip reaches to. This is an array of cartesian coordinates (x,y). This array is size $(M,2)$ with 2 being the number of positional coordinates.\n",
" \n",
" \n",
"* **wf**: This is an array of raw voltage snippets, representing the waveform for detected spike of each neuron in each electrode. The values are in microvolts. The size of this array is $(E,C,M)$.\n",
" \n",
" \n",
"* **Espks**: This is an array containing the times when spikes were detected for each neuron in each electrode. The values are in seconds. The size of this array is $(E,C,M)$, with $C$ being the number of Neurons detected. Note: in this variable, all original data is kept and no neuron spike arrays are discarded for being \"invalid.\"\n",
" \n",
" \n",
"* **Elabels**: The label/classification for available electrons (out of all original channels).\n",
" \n",
" \n",
"* **Mspks**: This array contains multi-unit spike data. I.e. times when spikes were detected on each electrode. Each element of this array is a vector of times for the detected spikes on an electrode. Invalid (pooled) electrode arrays are discarded. A valid neuron/electrode is one which has a neural firing rate of $\\ge0.5\\text{ Hz}$. The size of this array is $(E,M)$.\n",
" \n",
" \n",
"* **Sspks**: This array contains single-unit spike data. I.e. arrays of times when spikes were detected for each valid neuron. Each element of this array is a vector of times for the detected spikes for a single neuron. The size of this array is $(C,M)$."
]
},
{
"cell_type": "markdown",
"id": "660b2d80-a21d-4fd1-8f81-7a8638b54ba6",
"metadata": {},
"source": [
"### Conditioning Dataset for Decoding\n",
"\n",
"#### Obtaining All Kinematic States\n",
"\n",
"Notice that when loading data from a session, the *cursor_pos* variable only contains the cartesian coordinates in the *x* and *y* direction for the finger tip position. To get velocity in the *x* and *y* direction, the change in successive cursor positions in the *x* and *y* direction is taken and then divided by the sample time, or 4 ms. To get the acceleration in the *x* and *y* direction, the change in successive velocity positions in the *x* and *y* direction is taken and then divided by the sample time. This is defined in equations (1) and (2). This is also what is done in the custom \"bin_data\" function when the data output from the \"load_data\" function from the custom library is passed to it.\n",
"\n",
"$$x_{velo_i}[m]=\\frac{\\Delta x_{pos_i}[m]}{\\text {bin size}}\\tag{1}$$\n",
"\n",
"$$x_{acc_i}[m]=\\frac{\\Delta x_{velo_i}[m]}{\\text {bin size}}\\tag{2}$$\n",
"\n",
"where, $i$ is the respective coordinate ($x$ or $y$) and $m$ is the sample or time step in the session.\n",
"\n",
"#### Binning/Syncronyzing Variables:\n",
"\n",
"Note that the neural data collected in variables **Sspks**, **Espks**, and **Mspks** are just spike times and can occur at any given time. For example, the output of the code below shows a plot of all the arrays (SU spike arrays) in the **Espks** variable. Each dot in the plot represents a spike event. Each neuron spike array is offset along the y-axis with neurons from the same electrode being the same color and offset by a little. Neurons from different electrodes are offset by a more."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "5546ea88-fa31-47fd-be04-470a9f37f693",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# assign a color for each electrode channel\n",
"color = plt.cm.rainbow(np.linspace(0, 1, len(Espks)))\n",
"\n",
"# plot every neuron spike array\n",
"plt.figure(figsize=(13,8))\n",
"i = 0 # variable for offsetting spike dots\n",
"for electrode in Espks:\n",
" for neuron in electrode:\n",
" if (neuron is not None):\n",
" if (neuron.size > 1):\n",
" offs = np.ones(len(neuron)) + i*100\n",
" plt.plot(neuron, offs, '.', color=color[i], ms=2)\n",
" \n",
" i += 1\n",
" \n",
"plt.xlim([240,250])\n",
"plt.xticks(np.arange(240,250+1,1))\n",
"plt.grid()\n",
"plt.xlabel(\"Time Elapsed in Trial\\n(seconds)\", fontsize=20)\n",
"plt.xticks(fontsize=16)\n",
"plt.title(\"Electrode Spike Events vs. Time\", fontsize=24)\n",
"frame1 = plt.gca()\n",
"frame1.axes.yaxis.set_ticklabels([]);"
]
},
{
"cell_type": "markdown",
"id": "fbba019c-dd88-46a0-aba3-eab3952c4f9d",
"metadata": {},
"source": [
"*Figure 3. This plot is over a snippet of time for a trial in the [O'Doherty et al., 2020](https://zenodo.org/records/3854034) dataset. In this plot, each SU array from the dictionary data variable \"ESpks\" output from the custom \"load_data\" function is plotted. Each SU spike sorted neuron is offset vertically. The markers across the horizontal indicate when a spike detected event occurred for a neuron corresponding to the vertical position of those horizontal mark sets. Dot color is unique to each electrode here. If there is complete white space across, where a neuron spike array would be, it means no data was recorded for that neuron during this session.*\n",
"\n",
"As evident by the plot above, the neural spike data right now is comprised of all the times when spikes were detected (for electrodes/neurons) and are thus asynchronous. This means that the variables **Sspks**, **Espks**, and **Mspks** will not align with the kinematic measurements that are sampled synchronously at a steady sample rate of $250\\text{ Hz}$ (**cursor_pos**, **finger_pos**, and **target_pos**).\n",
"\n",
"This asynchonous data becomes a problem for the main goal of the decoder: that is, to predict the kinematic state at the next time step given the state of the current observed (neural) data and the current kinematic state estimate. It is not possible to make these predictions if a decoder's model works synchronously and the observed is not updated/measured synchronously at each time step.\n",
"\n",
"To convert the asynchronous neural spike data to synchronous data, the neural spikes can be \"binned.\" In binning, the time vector in a session is divided into fixed windows, or \"bins,\" and the neural data is converted into counts/number of spike occurances during each bin interval. In [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95), neural data was binned with bin intervals of $16\\text{ ms}$, $32\\text{ ms}$, $64\\text{ ms}$, and $128\\text{ ms}$. The same will be done in this project. It is also worth noting that these bin sizes will make the neural data periodic, but not at the same rate as the kinematic state measurements ($250\\text{ Hz}$). To align the kinematic measurements with this new binned data, the kinematic measurements can be downsampled from $250\\text{ Hz}$ to the new rate, $\\frac{1}{\\text{bin size}}$. To do the downsampling, a decimate function is called inside the \"bin_data\" function to ensure anti-aliasing of the kinematic states.\n",
"\n",
"The code below makes use of a custom function, \"bin_data,\" which does this binning and downsampling to sync up the data.\n",
"\n",
"#### Partitioning Dataset into Training and Evaluation Datasets:\n",
"\n",
"Another key concept is the idea of \"training\" and \"evaluation.\" The neural decoders are to predict kinematic states from neural observations from some model that relates the data. In order to develop that model, its parameters must be \"trained\" from data. Therefore, each session is broken up into training and evaluation sets. The training set is used strictly for model tuning/to learn parameters for the predictive model. The evaluation set is used in determining how well the model predicts the kinematic state from the observations in the evaluation set. For all sessions, the neural decoding models are trained on data collected up to the first 320 seconds of the session and evaluated on all data collected after that.\n",
"\n",
"On top of binning, the custom function \"bin_data\" (used below), splits the new synchronized data into \"train\" and \"test\" sets."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "f8bb48df-c938-43bd-85f2-2b162638ee7a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable | Size of Variable\n",
"kinematic_train | (6, 20001) \n",
"kinematic_test | (6, 31111) \n",
"su_spikes_train | (291, 20001) \n",
"su_spikes_test | (291, 31111) \n",
"mu_spikes_train | (141, 20001) \n",
"mu_spikes_test | (141, 31111) \n",
"t_train | (20001,) \n",
"t_test | (31111,) \n"
]
}
],
"source": [
"# binwidths used for binning neural spike data in Makin paper \n",
"bin_widths_ms = [16, 32, 64, 128]\n",
"\n",
"# bin spike data, resample all data to be in sync,\n",
"# and split into test and train sets\n",
"v = mt.bin_data(data,bin_width_ms=bin_widths_ms[0])\n",
"\n",
"\n",
"# extract variables from the binned data dictionary, v\n",
"## define print template and headers\n",
"printTemp = \"{0:15} | {1:15}\"\n",
"print(printTemp.format(\"Variable\", \"Size of Variable\"))\n",
"for key,val in v.items():\n",
" try:\n",
" print(printTemp.format(f\"{key}\",f\"{val.shape}\"))\n",
" except: \n",
" print(printTemp.format(f\"{key}\",f\"{len(val)}\"))"
]
},
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"This variables output here, from the code above and from the \"bin_data\" function, are all in sync and can now be used by the different decoders in training their models, making predictions, and collect results.\n",
"\n",
"These new synchronous variables output from the code above can be defined as:\n",
"\n",
"* **kinematic_train**: This is the ground truth kinematic states for the training set of the session and is sampled at $\\frac{1}{\\text{bin size}}$. This array has dimensions $(N,M)$, where $N$ is 6 for the kinematic states $x_{pos_x}$, $x_{pos_y}$, $x_{velo_x}$, $x_{velo_y}$, $x_{acc_x}$, and $x_{acc_y}$.\n",
" \n",
" \n",
"* **kinematic_test**: This is the ground truth kinematic states for the evaluation set of the session and is sampled at $\\frac{1}{\\text{bin size}}$. This array has dimensions $(N,M)$, where $N$ is 6 for the kinematic states $x_{pos_x}$, $x_{pos_y}$, $x_{velo_x}$, $x_{velo_y}$, $x_{acc_x}$, and $x_{acc_y}$.\n",
" \n",
" \n",
"* **su_spikes_train**: This is the binned single-unit neural spike counts of the training set. This array has dimensions $(C,M)$, where $C$ is the number of valid neurons.\n",
" \n",
" \n",
"* **su_spikes_test**: This is the binned single-unit neural spike counts of the evaluation set. This array has dimensions $(C,M)$, where $C$ is the number of valid neurons.\n",
" \n",
" \n",
"* **mu_spikes_train**: This is the binned multi-unit neural spike counts of the training set. This array has dimensions $(E,M)$, where $E$ is the number of electrodes.\n",
" \n",
" \n",
"* **mu_spikes_test**: This is the binned multi-unit neural spike counts of the evaluation set. This array has dimensions $(E,M)$, where $E$ is the number of electrodes.\n",
" \n",
" \n",
"* **t_train**: This is the time vector for the training set of the session and is sampled at $\\frac{1}{\\text{bin size}}$. This vector is length $M$.\n",
" \n",
" \n",
"* **t_test**: This is the time vector for the evaluation set of the session and is sampled at $\\frac{1}{\\text{bin size}}$. This vector is length $M$."
]
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"## Research Objectives\n",
"\n",
"The objectives for this project are to:\n",
"\n",
"1. Document and design and implement a Python library for conventional neural signal decoders for use with a published neural-kinematic dataset ([O'Doherty et al., 2020](https://zenodo.org/records/3854034)\n",
") This will be done by demonstrating similarity to the performance metrics in the results file accompanying the dataset (ideally the results here should match exactly). The results were collected by [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) and the different decoder implementations are described there as well. The authors for [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) mention that they share MATLAB code at [Makin and O’Doherty 2018](https://github.com/jgmakin/rbmish) for their implementations. The metrics being compared are signal-to-noise ratio (SNR) and coefficient of determination ($R^2$) and can be computed from the outputs of the decoder, or the kinematic state estimates, and the ground truth kinematic state evaluation data. These metrics are defined as:\n",
"\n",
"$$R^2 = 1 - \\frac{\\overline{(\\pmb{X} - \\pmb{\\hat{X}})^2}}{\\overline{(\\pmb{X} - \\bar{\\pmb{X}})^2}}\\tag{3}$$\n",
"\n",
"$$SNR = -10log_{10}(1-R^2)\\tag{4}$$\n",
"\n",
"where, bar ( $\\bar{}$ ) indicates sample average, hat ( $\\hat{}$ ) indicates estimate, and $\\pmb{X}$ are the kinematic states for all time steps in the evaluation partition for a session (nothing above $\\pmb{X}$ indicates ground-truth kinematic data).\n",
"\n",
"2. In objective 1 results are collected using single-unit neuron spike observations. After confirming that the decoders are implemented properly and match the results from a published paper that worked on the dataset, test the decoders ability to handle sub-optimal observational data. That is, simulate skipping of the spike sorting process by pooling all spike sorted data in each electrode. This will result in single spike arrays per electrode. Next, for the single-unit data, test the decoders ability to handle spikes that are missing by (uniform) randomly dropping spikes at different rates for a session. The drop rates to test for the sessions will be $5$ %, $15$ %, $25$ %, and $50$ %. For both the multi-unit (spike pooling) and spike dropping cases, quantify any improvements and/or by how much the decoders performance changed from single-unit baseline case with no dropping or pooling."
]
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"# Methods\n",
"\n",
"The neural decoders are of main focus in [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) and this extended effort. Due to the limited time-frame at the time of this Master's Project, all decoders from [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) were not fully implemented. Only three decoders were implemented. The decoders featured here are regression, Kalman filter supervised, and Kalman filter unsupervised with static mapping (from latent to ground-truth). Prior to presenting these decoders, it is worth defining variable and notation definitions:\n",
"\n",
"## Notations and Variable Definitions:\n",
"\n",
"| Notation | Definition |\n",
"| --- | --- |\n",
"| lower case, non-bold, letter (e.g. $x$) | scalar |\n",
"| lower case, bold, letter (e.g. $\\textbf{x}$) | vector |\n",
"| upper case, bold, letter (e.g. $\\textbf{X}$) | matrix |\n",
"| Hat (e.g. $\\hat{\\textbf{X}})$ | Estimate |\n",
"| Bar (e.g. $\\bar{\\textbf{X}}$) | Sample Average |\n",
"| Single Quotation (e.g. $\\textbf{X}'$) | Transpose of Matrix/Vector |\n",
"| Bar Power, Subscript $m$ (e.g. $\\hat{\\textbf{x}}_{m}^{-}$) | Is a prediction (made for time step $m$) |\n",
"| $E[\\text{ }]$ | Expectation |\n",
"| $Cov[\\text{ }]$ | Covariance |\n",
"\n",
"| Variable | Size | Definition |\n",
"| ---------- | ---------------- | ------------------------------------------------------------------------------------------------------------------ |\n",
"| $M$ | scalar | Number of Samples. |\n",
"| $C$ | scalar | Number of Observed Neurons. |\n",
"| $E$ | scalar | Number of Observed Electrodes. |\n",
"| $N$ | scalar | Number of States to be predicted (e.g. # Kinematic states, # Latent states).|\n",
"| $x_{n,m}$ | scalar | $n$-th Kinematic State at time step $m$. |\n",
"| $\\textbf{x}_m$ | ($N$,$1$) | Kinematic State Vector at time step $m$. |\n",
"| $\\textbf{X}$ | ($N$,$M$) | Kinematic State Matrix (collection of $M$ successive Kinematic State Vectors). |\n",
"| $\\textbf{X}_1$ | ($N$,$M-1$) | Current time step's Kinematic State Matrix (collection of Kinematic State Vectors measured/estimated at each of the $M-1$ time steps for that time step). |\n",
"| $\\textbf{X}_2$ | ($N$,$M-1$) | Next time step's Kinematic State Matrix (collection of Kinematic State Vectors measured/estimated at each of the $M-1$ time steps for the next time step). |\n",
"| $z_{n,m}$ | scalar | $n$-th Latent State at time step $m$. |\n",
"| $\\textbf{z}_m$ | ($N$,$1$) | Latent State Vector at time step $m$. |\n",
"| $\\textbf{Z}$ | ($N$,$M$) | Latent State Matrix (collection of $M$ successive Latent State Vectors). |\n",
"| $\\textbf{Z}_1$ | ($N$,$M-1$) | Current time step's Latent State Matrix (collection of Latent State Vectors measured/estimated at each of the $M-1$ time steps for that time step). |\n",
"| $\\textbf{Z}_2$ | ($N$,$M-1$) | Next time step's Latent State Matrix (collection of Latent State Vectors measured/estimated at each of the $M-1$ time steps for the next time step).|\n",
"| $r_{c,m}$ | scalar | $c$-th Observation at time step $m$.|\n",
"| $\\textbf{r}_m$ | ($C$,$1$) | Observation Vector at time step $m$. |\n",
"| $\\textbf{R}$ | ($C$,$M$) | Observation Matrix (collection of $M$ successive Observation Vectors). |\n",
"| $\\pmb{\\beta}$ | ($N$,$C+1$) | Parameter Matrix used in the Linear Regression Decoder and a constant employed in Factor Analysis. |\n",
"| $\\epsilon$ | N/A | Noise Distribution used in the Linear Regression Decoder and Factor Analysis. |\n",
"| $\\pmb{\\Psi}$ | ($C$,$C$) | The covariance matrix for the generative LGDS model used in Factor Analysis. |\n",
"| $\\textbf{H}$ | ($C$,$N$) | Observation Matrix for the LGDS Neural-Kinematic (or Latent) model. |\n",
"| $\\textbf{A}$ | ($N$,$N$) | State Transition Matrix for the LGDS Neural-Kinematic (or Latent) model. |\n",
"| $\\textbf{h}$ | ($C$,$1$) | Observation Offset Vector for the LGDS Neural-Kinematic (or Latent) model. |\n",
"| $\\textbf{a}$ | ($N$,$1$) | State Offset Vector for the LGDS Neural-Kinematic (or Latent) model. |\n",
"| $\\textbf{q}$ | N/A | Is the observation noise associated with the LGDS Neural-Kinematic (or Latent) model. |\n",
"| $\\textbf{Q}$ | ($C$,$C$) | Observation Covariance Matrix for the LGDS Neural-Kinematic (or Latent) model. |\n",
"| $\\textbf{w}$ | N/A | Is the process noise associated with the LGDS Neural-Kinematic (or Latent) model. |\n",
"| $\\textbf{W}$ | ($N$,$N$) | State Covariance Matrix for the LGDS Neural-Kinematic (or Latent) model. |\n",
"| $\\textbf{P}^{-}_{m}$ | ($N$,$N$) | State Estimate Covariance Matrix prediction for the $m$-th step of the KF decoder ($m=0$ is the prior prediction).|\n",
"| $\\pmb{\\hat{x}}^{-}_{m}$ | ($N$,$N$) | State Estimate prediction for the $m$-th step of the KF decoder ($m=0$ is the prior prediction). |\n",
"| $\\pmb{K}_m$ | ($N$,$C$) | Kalman Gain at time step $m$ for the KF decoder. |\n",
"| $\\pmb{P}_m$ | ($N$,$C$) | State Estimate Covariance Matrix for the $m$-th step of the KF decoder. |\n",
"| $\\textbf{L}$ | ($C$,$N$) | Factor Loading Matrix used in the Factor Analysis LGDS model. |\n",
"| $\\pmb{\\mu}$ | ($C$,$1$) | Mean of the observational data (used in Factor Analysis). |\n",
"| $\\textbf{J}_{m}$ | ($N$,$N$) | Smoother Gain Matrix used in the KF Unsupervised Decoder. |\n",
"| $\\pmb{\\hat{z}}_{sm_{m}}$ | ($N$,$1$) | Smoothed/Refined Latent State Estimates (used in KF Unsupervised Decoder). |\n",
"| $\\textbf{P}_{sm_{m-1}}$ | ($N$,$N$) | Smoothed/Refined Latent State Covariance Estimates (used in the KF Unsupervised Decoder). |\n",
"| $\\textbf{P}_{sm_{m,m-1}}$ | ($N$,$N$) | Smoothed/Refined Latent State Covariance Extrapolation Estimates (used in the KF Unsupervised Decoder). |\n",
"| $\\textbf{P}_{m,m-1}$ | ($N$,$N$) | Expected State Extrapolation Covariance Matrix (used in KF Unsupervised Decoder). |\n",
"| $\\textbf{D}$ | ($N$,$N_{latent}$) | Static Mapping Matrix that transforms the latent states to the kinematic states (used in KF Unsupervised Static Mapping Decoder). |\n",
"| $\\textbf{d}$ | ($N$,$1$) | Static Mapping Offset Vector that completes the linear transformation from latent state to kinematic states (used in KF Unsupervised Static Mapping Decoder). |\n",
"| $\\textbf{I}_N$ | ($N$,$N$) | Identity Matrix. |\n",
"\n",
"**Note**: In the above:\n",
"\n",
"* If considering multi-unit data, the dimension $C$ for parameters changes to $E$.\n",
"\n",
"## Neural Decoders:\n",
"\n",
"### Regression\n",
"#### Linear Model\n",
"\n",
"Regression is a method that essentially finds a line-of-best-fit between one variable and another. For this application, the kinematic states at time $m$, or $\\pmb{x}_m$, is regressed on the neuron firing rates at time $m$, or $\\pmb{r}_m$, via linear regression. This regression can be described mathematically as:\n",
"\n",
"$$\\pmb{X} = \\pmb{\\beta} \\pmb{R}_A + \\epsilon\\tag{5}$$\n",
"\n",
"where:\n",
"\n",
"$\\pmb{X} = [\\pmb{x}_1, \\pmb{x}_2, ..., \\pmb{x}_M] \\in (N,M)$\n",
"\n",
"$\\pmb{R}_A = [\\pmb{1}; \\pmb{R}] \\in (C+1,M)$\n",
"\n",
"$\\pmb{R} = [\\pmb{r}_1, \\pmb{r}_2, ..., \\pmb{r}_M] \\in (C,M)$\n",
"\n",
"$\\pmb{\\beta} = [\\pmb{\\beta}_0, \\pmb{\\beta}_1, \\pmb{\\beta}_2, ..., \\pmb{\\beta}_C] \\in (N,C+1)$\n",
"\n",
"with,\n",
"\n",
"$\\pmb{x}_m = [x_{1,m}, x_{2,m}, ..., x_{N,m}]^T$ being a column vector with $N$ kinematic states at time $m$.\n",
"\n",
"$\\pmb{r}_m = [r_{1,m}, r_{2,m}, ..., r_{C,m}]^T$ being a column vector with $C$ observations (binned neural spikes) at time $m$.\n",
"\n",
"$\\pmb{\\beta}_c = [\\beta_{1,c}, \\beta_{2,c}, ..., \\beta_{N,c}]^T, \\text{ }c\\ne0,$ being a column vector comprised of $N$ coefficients. The $n$-th coefficient in this vector represents essentially how much the $c$-th observation contributes toward the transformation to the $n$-th kinematic state.\n",
"\n",
"$\\pmb{\\beta}_0 = [\\beta_{1,0}, \\beta_{2,0}, ..., \\beta_{N,0}]^T$ being an $N$ element column vector with each element representing an offset. Each offset in the vector is positioned with respect to the state that it is to complete the linear transformation for.\n",
"\n",
"Note that the rows of $\\pmb{\\beta}$ are comprised of the coefficients that transform (collectively) the $m$-th observation vector, $\\pmb{r}_m$, to the $n$-th state at time $m$, or $x_{n,m}$. Each row of $\\pmb{\\beta}$ is made up of weights that determine how much each observed element (respective to coefficient element) contribute in the transformation to the output state, $n$. The $\\beta_{n,0}$ term of that row combines with a 1 to scale up or down the transformation by $\\beta_{n,0}$ to compensate for an expected offset.\n",
"\n",
"$\\epsilon$ is the unaccounted for noise associated with the model (needed to make the model exact).\n",
"\n",
"Now, to solve for the parameters, $\\pmb{\\beta}$ of the model, an objective function, $S(\\pmb{\\beta})$ is used. The objective will be to solve for the $\\pmb{\\beta}$ that minimizes the mean square of the error, or:\n",
"\n",
"$$S(\\pmb{\\beta}) = \\|\\pmb{X} - \\pmb{\\beta} \\pmb{R}_A\\|^2\\tag{6}$$\n",
"\n",
"expanding this:\n",
"\n",
"$$= (\\pmb{X} - \\pmb{\\beta} \\pmb{R}_A)(\\pmb{X} - \\pmb{\\beta} \\pmb{R}_A)'$$\n",
"\n",
"$$= \\pmb{X}\\pmb{X}' - \\pmb{X} \\pmb{R}_A' \\pmb{\\beta}' - \\pmb{\\beta} \\pmb{R}_A \\pmb{X}' + \\pmb{\\beta} \\pmb{R}_A \\pmb{R}_A' \\pmb{\\beta}'$$\n",
"\n",
"#### Training\n",
"\n",
"To minimize this, take the derivative with respect to $\\pmb{\\beta}$ and set this equal to 0:\n",
"\n",
"$$\\frac{dS(\\pmb{\\beta})}{d\\pmb{\\beta}} = -\\pmb{X} \\pmb{R}_A' - \\pmb{R}_A \\pmb{X}' + 2 \\pmb{\\beta} \\pmb{R}_A \\pmb{R}_A' = 0$$\n",
"\n",
"$$= -2\\pmb{X} \\pmb{R}_A' + 2 \\pmb{\\beta} \\pmb{R}_A \\pmb{R}_A'$$\n",
"\n",
"$$\\Rightarrow \\pmb{X} \\pmb{R}_A' = \\pmb{\\beta} \\pmb{R}_A \\pmb{R}_A'$$\n",
"\n",
"$$\\Rightarrow \\boxed{\\pmb{\\beta} = \\pmb{X} \\pmb{R}_A' (\\pmb{R}_A \\pmb{R}_A')^{-1}}\\tag{7}$$\n",
"\n",
"This is called a least squares approximation.\n",
"\n",
"Finally, note that to solve equation 5, data must be collected to fill out the $\\pmb{X}$ and $\\pmb{R}$ matrices. This is done over a subset of the session data. In [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95), this subset was defined as the first $320\\text{ seconds}$ of the session. This subset is called \"training\" data and the same approach is applied here. All data following that is called \"test\"/\"evaluation\" data. Following training, and with the \"test\" subset, the neural data is plugged into equation 5 above (neglecting $\\epsilon$) and kinematic states, $\\pmb{\\hat{X}}$, for the \"test\" subset are predicted and compared to the true data from the experiment. $SNR$ and $R^2$ metrics are used to compare how well this decoder works. The code below does the training and evaluation for all session kinematic state/bin width combinations for the regression decoder."
]
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"#### Run Regression Implementation and Log Results:\n",
"\n",
"The first code snippet below will collect results for the main experiment, or the one that validates the implementation for the decoders (the SU results that are reported by [O'Doherty et al., 2020](https://zenodo.org/records/3854034)\n",
") The next code snippet collects the multi-unit results for the regression decoder, or that when the neural data input to the regression model are the pooled electrode spikes (spike sorting abandoned). The last code snippet collects the results for the four spike dropped cases for the regression decoder ($5$ %, $15$ %, $25$ %, and $50$ % of spikes dropped)."
]
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"source": [
"##### Single-Unit Regression Results:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "51926af9-db00-4307-8c43-3ef5505e6da2",
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"1124 15338 velx 128 regression -0.045028 \n",
"1125 15338 vely 128 regression 0.222251 \n",
"1126 15338 accx 128 regression -0.254173 \n",
"1127 15338 accy 128 regression -0.109364 \n",
"\n",
" snr \n",
"0 0.332903 \n",
"1 0.474428 \n",
"2 0.973442 \n",
"3 1.210485 \n",
"4 0.191084 \n",
"... ... \n",
"1123 1.005079 \n",
"1124 -0.191278 \n",
"1125 1.091606 \n",
"1126 -0.983573 \n",
"1127 -0.450740 \n",
"\n",
"[1128 rows x 10 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resultsDir = os.getcwd() + r\"\\single_unit_results\"\n",
"df_regress = mt.collectResults(decoder=\"regression\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDir,\\\n",
" bMU=False, dropPercent=0,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=0,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"df_regress"
]
},
{
"cell_type": "markdown",
"id": "79241a63-bd4f-4595-8570-3171b2363bde",
"metadata": {},
"source": [
"##### Multi-Unit Regression Results:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "ba3981dc-371f-45c3-ad8e-42d2ee0c2e72",
"metadata": {},
"outputs": [],
"source": [
"resultsDirMU = os.getcwd() + r\"\\multi_unit_results\"\n",
"df_regressMU = mt.collectResults(decoder=\"regression\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirMU,\\\n",
" bMU=True, dropPercent=0,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=0,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)"
]
},
{
"cell_type": "markdown",
"id": "ffe3cfbc-26e1-42cc-97a9-58fc37fdc3d8",
"metadata": {},
"source": [
"##### Spike Dropped Regression Results:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "1d96f6f2-ce3b-468e-a01d-4b6be8f4eea4",
"metadata": {},
"outputs": [],
"source": [
"resultsDirDrop = os.getcwd() + r\"\\dropped_spikes_results\"\n",
"df_regressD05 = mt.collectResults(decoder=\"regression\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=5,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_regressD15 = mt.collectResults(decoder=\"regression\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=15,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_regressD25 = mt.collectResults(decoder=\"regression\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=25,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_regressD50 = mt.collectResults(decoder=\"regression\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=50,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)"
]
},
{
"cell_type": "markdown",
"id": "c5003ed8-a31e-451e-8846-8e46169dbcdd",
"metadata": {},
"source": [
"### Kalman Filter Supervised/Observed\n",
"\n",
"#### LGDS model\n",
"\n",
"The Kalman Filter (KF) decoder is one which employs knowledge of a system's dynamical model, noisy measurements, and the uncertainties (or noise) in the model and measurements to make an estimate about a system's state(s) over time. By using all of this knowledge, the KF is able to make an informed estimate about the system state(s), resulting in better estimation when compared to estimating from measurements alone. The KF typically involves a linear gaussian dynamical system (LGDS). For this application, a state-space LGDS model is defined and used in the KF decoder, specifically:\n",
"\n",
"$$\\pmb{r}_m = \\pmb{H}\\pmb{x}_m + \\pmb{h} + q\\tag{8}$$\n",
"$$\\pmb{x}_{m+1} = \\pmb{A}\\pmb{x}_m + \\pmb{a} + w\\tag{9}$$\n",
"\n",
"Equation (6) is defined as the measurement equation and equation (7) is defined as the state extrapolation equation. The parameters for those equations can be defined as,\n",
"\n",
"$\\pmb{H} \\in (C,N)$ is the observation matrix, which transforms the state vector, $\\pmb{x}_m$, to the observation space.\n",
"\n",
"$\\pmb{A} \\in (N,N)$ is the state transition matrix, which transforms the state vector at the current time ($m$), $\\pmb{x}_m$, to the space associated with the state vector at the very next time step ($m+1$), $\\pmb{x}_{m+1}$.\n",
"\n",
"$\\pmb{h} \\in (C,1)$ is the observation offset vector or mean and scales up or down $\\pmb{H}\\pmb{x}_m$ to complete the transformation from $\\pmb{x}_m$ to $\\pmb{r}_m$.\n",
"\n",
"$\\pmb{a} \\in (N,1)$ is the state offset vector or mean and scales up or down $\\pmb{A}\\pmb{x}_m$ to complete the transformation from $\\pmb{x}_m$ to $\\pmb{x}_{m+1}$.\n",
"\n",
"$q \\sim N(0,\\pmb{Q})$ is the observation noise and essentially informs the KF as to how much trust can be given into computing an estimate from the measurements alone. $\\pmb{Q} \\in (C,C)$ is the observation covariance.\n",
"\n",
"$w \\sim N(0,\\pmb{W})$ is the process noise and essentially informs the KF as to how much trust can be given into computing an estimate from the model alone. $\\pmb{W} \\in (N,N)$ is the state covariance.\n",
"\n",
"Note: While this model could be time-varying, where the parameters change with time, the system here is assummed time-invariant with independent and identically distributed (IID) random variables. I.e., the parameters of the model $\\pmb{H}$, $\\pmb{h}$, $\\pmb{Q}$, $\\pmb{A}$, $\\pmb{a}$, and $\\pmb{W}$ are constant.\n",
" \n",
" \n",
"\n",
"#### Training\n",
"\n",
"In the Kalman Filter observed, the parameters will be learned from the training dataset (i.e. the first 320 seconds of a session) using the actual ground truth kinematic states--$\\pmb{X}$. The $\\pmb{H}$, $\\pmb{h}$, $\\pmb{A}$, and $\\pmb{a}$ parameters will be learned via linear regression using a least squares approximation (as derived in the \"regression\" decoder section). The $\\pmb{Q}$ and $\\pmb{W}$ covariance matricies can then be found by squaring the errors (difference in actual from the approximation by the LGDS model), normalized by the number of samples used, $M$. I.e., these parameters can be found as follows:\n",
"\n",
"$$[\\pmb{h}, \\pmb{H}] = \\pmb{R} \\pmb{X}_A'(\\pmb{X}_A\\pmb{X}_A')^{−1}\\tag{10}$$ \n",
" \n",
"$$[\\pmb{a}, \\pmb{A}] = \\pmb{X}_2 \\pmb{X}_{A_1}'(\\pmb{X}_{A_1} \\pmb{X}_{A_1}')^{-1}\\tag{11}$$\n",
"\n",
"$$\\pmb{Q} = \\frac{(\\pmb{R}-(\\pmb{H}\\pmb{X}+\\pmb{h}))(\\pmb{R}-(\\pmb{H}\\pmb{X}+\\pmb{h}))^T}{M}\\tag{10}$$ \n",
"\n",
"$$\\pmb{W} = \\frac{(\\pmb{X}_2 - (\\pmb{A}\\pmb{X}_1 + \\pmb{a}))(\\pmb{X}_2 - (\\pmb{A}\\pmb{X}_1 + \\pmb{a}))^T}{M-1}\\tag{12}$$ \n",
"\n",
"where, in the above:\n",
"\n",
"$\\pmb{X}_A = [1; \\pmb{X}] \\in (N+1,M)$\n",
"\n",
"$\\pmb{X}_{A_1} = [1; \\pmb{X}_1] \\in (N+1,M-1)$\n",
"\n",
"$\\pmb{X}_1 = [\\pmb{x}_1, \\pmb{x}_2, ..., \\pmb{x}_{M-1}] \\in (N,M-1)$\n",
"\n",
"$\\pmb{X}_2 = [\\pmb{x}_2, \\pmb{x}_3, ..., \\pmb{x}_M] \\in (N,M-1)$\n",
"\n",
"$\\pmb{X}$ and $\\pmb{X}_1$ are prepended with a row of 1's to form $\\pmb{X}_A$ and $\\pmb{X}_{A_1}$ so that the offset vectors $\\pmb{h}$ and $\\pmb{a}$ can be found. These offset vectors are the first columns of the output matrix in equations (8) and (9), respectively.\n",
" \n",
" \n",
"\n",
"#### Estimation\n",
"\n",
"After learning the parameters, the KF can be implemented on the test data in the following series of steps:\n",
"\n",
"##### Initialize\n",
"Time step is $0$ here.\n",
"\n",
"Make assumptions on prior knowledge (*a priori*) for the first outcome of a trial. I.e. What is the first estimated state prediction ($\\pmb{\\hat{x}}_{0}^{-}$)? What is the uncertainty in that initial prediction ($\\pmb{P}^{-}_{0}$)?\n",
"\n",
"In Makin et al., 2018, the initial state and uncertainty in the initial state is assumed as the mean and convariance, respectively, over all kinematic state measurements from the training dataset, or:\n",
"\n",
"$$\\pmb{\\hat{x}}_{0}^{-} = \\bar{\\pmb{X}}\\tag{13}$$\n",
"\n",
"$$\\pmb{P}^{-}_{0} = \\text{cov}(\\pmb{X},\\pmb{X})\\tag{14}$$\n",
"\n",
"where, equation (12) is the sample average for each state vector in the training set (i.e. average across columns of $\\pmb{X}$) and $\\pmb{\\hat{x}}_{0}^{-} \\in (N,1)$ and $\\pmb{P}^{-}_{0} \\in (N,N)$.\n",
" \n",
" \n",
"\n",
"##### Measure\n",
"\n",
"Increment to newest time step and collect this time step's neural observation vector, $\\pmb{r}_m$.\n",
" \n",
" \n",
"\n",
"##### Update (*posteriori*)\n",
"Solve the following Equations:\n",
"\n",
"*Calculate the Kalman Gain (***Kalman Gain Equation***)*:\n",
"$$\\pmb{K}_m = \\pmb{P}^{-}_m\\pmb{H}'(\\pmb{H}\\pmb{P}^{-}_m\\pmb{H}' + \\pmb{Q})^{-1}\\tag{15}$$\n",
"\n",
"*Estimate the Current State (***State Update Equation***)*:\n",
"$$\\pmb{\\hat{x}}_m = \\pmb{\\hat{x}}^{-}_m + \\pmb{K}_m(\\pmb{r}_m - (\\pmb{H}\\pmb{\\hat{x}}^{-}_m + \\pmb{h}))\\tag{16}$$\n",
"\n",
"*Update the Current Estimate Uncertainty (***Covariance Update Equation***)*:\n",
"$$\\pmb{P}_m = (\\pmb{I}_N - \\pmb{K}_m\\pmb{H})\\pmb{P}^{-}_m\\tag{17}$$\n",
"\n",
"where, $\\pmb{K}_m \\in (N,C)$ is a matrix that dynamically updates at each time step. It essentially weights the prediction and the result from measured data at time step $m$ to determine how much of each to use in computing the final estimate for state and uncertainty at time $m$.\n",
" \n",
" \n",
"\n",
"##### Predict the Next State and Estimate Uncertainty in that Prediction (*a priori*):\n",
"Solve the following Equations:\n",
"\n",
"*Predict the next state (***State Extrapolation Equation***)*:\n",
"$$\\pmb{\\hat{x}}_{m+1}^{-} = \\pmb{A}\\pmb{\\hat{x}}_{m} + \\pmb{a}\\tag{18}$$\n",
"\n",
"*Extrapolate Estimate Uncertainty (***Covariance Extrapolation Equation***)*:\n",
"$$\\pmb{P}^{-}_{m+1} = \\pmb{A}\\pmb{P}_{m}\\pmb{A}' + \\pmb{W}\\tag{19}$$\n",
" \n",
" \n",
"\n",
"##### Repeat steps 2-4 for Each Time Step in the test trial."
]
},
{
"cell_type": "markdown",
"id": "c5dca7e7-cf77-434f-a839-b17f1dc85364",
"metadata": {},
"source": [
"#### Run Kalman Filter Supervised Implementation and Log Results:\n",
"\n",
"The first code snippet below will collect results for the main experiment, or the one that validates the implementation for the decoders (the SU results that are reported by [O'Doherty et al., 2020](https://zenodo.org/records/3854034)). The next code snippet collects the multi-unit results for the KF supervised (or KF observed) decoder, or that when the neural data input to the KF supervised model are the pooled electrode spikes (spike sorting abandoned). The last code snippet collects the results for the four spike dropped cases for the KF supervised decoder ($5$ %, $15$ %, $25$ %, and $50$ % of spikes dropped)."
]
},
{
"cell_type": "markdown",
"id": "f00977c6-9a1e-4b73-9fd7-8cd94acb47ec",
"metadata": {},
"source": [
"##### Single-Unit KF Supervised Results:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2518a1f0-5842-4013-9b71-5ef9957bfbb4",
"metadata": {},
"outputs": [
{
"data": {
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loco_20170302_02
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loco_20170302_02
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1128 rows × 10 columns
\n",
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"
],
"text/plain": [
" session monkey num_neurons num_training_samples \\\n",
"0 indy_20160407_02 indy 291 20001 \n",
"1 indy_20160407_02 indy 291 20001 \n",
"2 indy_20160407_02 indy 291 20001 \n",
"3 indy_20160407_02 indy 291 20001 \n",
"4 indy_20160407_02 indy 291 20001 \n",
"... ... ... ... ... \n",
"1123 loco_20170302_02 loco 500 2501 \n",
"1124 loco_20170302_02 loco 500 2501 \n",
"1125 loco_20170302_02 loco 500 2501 \n",
"1126 loco_20170302_02 loco 500 2501 \n",
"1127 loco_20170302_02 loco 500 2501 \n",
"\n",
" num_testing_samples kinematic_axis bin_width decoder rsq \\\n",
"0 31111 posx 16 KF_observed 0.654239 \n",
"1 31111 posy 16 KF_observed 0.720598 \n",
"2 31111 velx 16 KF_observed 0.464961 \n",
"3 31111 vely 16 KF_observed 0.519963 \n",
"4 31111 accx 16 KF_observed 0.019262 \n",
"... ... ... ... ... ... \n",
"1123 15338 posy 128 KF_observed 0.465985 \n",
"1124 15338 velx 128 KF_observed 0.063036 \n",
"1125 15338 vely 128 KF_observed 0.398612 \n",
"1126 15338 accx 128 KF_observed -0.184109 \n",
"1127 15338 accy 128 KF_observed 0.006796 \n",
"\n",
" snr \n",
"0 4.612244 \n",
"1 5.537712 \n",
"2 2.716147 \n",
"3 3.187252 \n",
"4 0.084469 \n",
"... ... \n",
"1123 2.724469 \n",
"1124 0.282771 \n",
"1125 2.208456 \n",
"1126 -0.733918 \n",
"1127 0.029615 \n",
"\n",
"[1128 rows x 10 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resultsDir = os.getcwd() + r\"\\single_unit_results\"\n",
"df_kfObs = mt.collectResults(decoder=\"KF_observed\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDir,\\\n",
" bMU=False, dropPercent=0,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=0,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_kfObs"
]
},
{
"cell_type": "markdown",
"id": "d5f0606a-a728-4955-a4d6-c0f466f3d72b",
"metadata": {},
"source": [
"##### Multi-Unit KF Supervised Results:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "a73383c2-91d1-4927-9d46-f77afc08024f",
"metadata": {},
"outputs": [],
"source": [
"resultsDirMU = os.getcwd() + r\"\\multi_unit_results\"\n",
"df_kfObsMU = mt.collectResults(decoder=\"KF_observed\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirMU,\\\n",
" bMU=True, dropPercent=0,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=0,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)"
]
},
{
"cell_type": "markdown",
"id": "d1333545-2abe-4210-a0fc-e95fac28c709",
"metadata": {},
"source": [
"##### Spike Dropped KF Supervised Results:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "d93d70bb-6a0e-4621-a599-9a9674fb57db",
"metadata": {},
"outputs": [],
"source": [
"resultsDirDrop = os.getcwd() + r\"\\dropped_spikes_results\"\n",
"df_kfObsD05 = mt.collectResults(decoder=\"KF_observed\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=5,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_kfObsD15 = mt.collectResults(decoder=\"KF_observed\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=15,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_kfObsD25 = mt.collectResults(decoder=\"KF_observed\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=25,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_kfObsD50 = mt.collectResults(decoder=\"KF_observed\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=50,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)"
]
},
{
"cell_type": "markdown",
"id": "cbcc2544-2a0f-4845-ad6f-e366475ac4e8",
"metadata": {},
"source": [
"### Kalman Filter Unsupervised/Latent\n",
"\n",
"#### LGDS Model\n",
"\n",
"The KF unsupervised model assumes kinematic states are not measured and known. This decoder uses intermediate \"latent\" states which are estimated first from observed neural data. Then, from the \"latent\" states, the kinematic states are estimated. For this model, the LGDS is as follows:\n",
"\n",
"$$\\pmb{r}_m = \\pmb{H}\\pmb{z}_m + \\pmb{h} + q\\tag{20}$$\n",
"$$\\pmb{z}_{m+1} = \\pmb{A}\\pmb{z}_m + \\pmb{a} + w\\tag{21}$$\n",
"\n",
"These are the same equations as (6) and (7), with the exception being that the kinematic states, $\\pmb{x}$, are replaced with the latent states, $\\pmb{z}$."
]
},
{
"cell_type": "markdown",
"id": "7f2b8c91-fd2d-4d4b-94b7-4a2d3df064b5",
"metadata": {},
"source": [
"#### Training\n",
"\n",
"As stated before, the assumption with KF unsupervised is that the ground truth states, or the kinematic states, are not available. Thus, the training from the supervised case cannot be applied here. Instead an Expectation-Maximization (EM) algorithm is applied to learn the parameters that maximize the probability of observing the neural states in the training partition."
]
},
{
"cell_type": "markdown",
"id": "9611a03e-5b61-47f7-b31a-b229b6ec88e3",
"metadata": {},
"source": [
"##### Perform Factor Analysis to Initialize the Latent States, their Uncertainty, and the Parameters for the LGDS\n",
"\n",
"Prior to applying the EM algorithm for parameter discovery, initial values are to be assumed for the LGDS parameters and the latent states, $\\pmb{Z}$ (for the training partition). Factor Analysis can be applied to perform initialization and help in preventing convergence to local maxima in the actual EM training to be applied for discovery of most likely LGDS parameters (section 3.2.2.). Factor Analysis will work to discover a generative model for the observational data (or the likelihood, $\\mathcal{L}(\\pmb{Z}|\\pmb{R})$) and a generative model for the latent states (or the posterior, $p(\\pmb{Z}|\\pmb{R})$). Prior to applying factor analysis, some assumptions will be made that were made in [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95): first, the number of latent states will be set to one third the number of observations (or $\\frac{1}{3}C$) (this was said to provide \"the best results\" for this dataset); secondly, to avoid over-fitting, the state ($\\pmb{W}$) and observation ($\\pmb{Q}$) covariance matrices are assumed diagonal. Factor Analysis models the observation, $\\pmb{r}_m$, as a Linear-Gaussian generative model:\n",
"\n",
"$$\\pmb{r}_m = \\pmb{L} \\pmb{z}_m + \\pmb{\\mu} + \\epsilon\\tag{22}$$\n",
"\n",
"where,\n",
"\n",
"$\\pmb{L} \\in (C,N)$ is a factor loading matrix (where, here $N = \\frac{1}{3}C$);\n",
"\n",
"$\\pmb{\\mu} \\in (C,1)$ is a constant, whose maximum likelihood estimator is the mean of the neural observation data (set to $\\bar{\\pmb{R}}$ for the training partition of the session to be decoded);\n",
"\n",
"$\\epsilon$ is the error or noise in the observations, where $\\epsilon$~$N(0,\\pmb{\\Psi})$, with $\\pmb{\\Psi}$ being diagonal;\n",
"\n",
"the prior distribution for the latent states is assumed:\n",
"\n",
"$$p(\\pmb{z}_m)\\sim N(0\\text{, }\\pmb{I})\\tag{23}$$\n",
"\n",
"the generative model for the observations, or likelihood at time step $m$ is thus:\n",
"\n",
"$$\\mathcal{L}(\\pmb{z}_m|\\pmb{r}_m)= p(\\pmb{r}_m|\\pmb{z}_m)\\sim N(\\pmb{L}\\pmb{z}_m + \\pmb{\\mu}\\text{, }\\pmb{\\Psi})\\tag{24}$$\n",
"\n",
"With this knowledge, the posterior distribution can be deduced as (see appendix for derivation):\n",
"\n",
"$$p(\\pmb{z}_m|\\pmb{r}_m)\\sim N(\\pmb{\\beta}(\\pmb{r}_m-\\mu)\\text{, }\\pmb{I}_N-\\pmb{\\beta} \\pmb{L})\\tag{25}$$\n",
"\n",
"with $\\pmb{\\beta} = \\pmb{L}'(\\pmb{L}\\pmb{L}'+ \\pmb{\\Psi})^{-1}$.\n",
" \n",
" \n",
" \n",
"Factor Analysis iterates between an \"E\" and an \"M\" step (and checks for convergence following that) The algorithm can be summarized as follows:\n",
"\n",
"0. **Initialization Step** (initialize parameters for the model). The \"E\" step will need the LGDS parameters to compute the expectation values for the latent state, so initialize parameters $\\pmb{L}$ and $\\pmb{\\Psi}$ here. This step is done only initially and then steps 1-3 are iterated over repeatedly. In this project, $\\pmb{\\Psi}$ is initialized as the sample variance computed over the training observation partition. The factor loading matrix $\\pmb{L}$ is initialized by with the \"Loading\" matrix from Principal Components Analysis (PCA) (see [Centellegher 2023](https://scentellegher.github.io/machine-learning/2020/01/27/pca-loadings-sklearn.html)). This is done by computing the singular value decomposition (SVD) on the observational training partition and using the SVD matricies to compute the \"Loading\" matrix, as follows:\n",
"\n",
" Compute SVD:\n",
"\n",
" $$\\pmb{R}=\\pmb{U}\\pmb{S}\\pmb{V}'$$\n",
"\n",
" * $\\pmb{U}$ matrix contains the left singular vectors (or \"principal components\") of $\\pmb{R}$.\n",
" * $\\pmb{S}$ matrix contains the singular values of $\\pmb{R}$.\n",
" * $\\pmb{V}$ matrix has columns which contain the \"principal axes\" of $\\pmb{R}$.\n",
" \n",
" The $\\pmb{S}$ and $\\pmb{V}$ matricies can be combined to compute Loading Matrix:\n",
"\n",
" $$\\pmb{L}=\\pmb{V}\\frac{\\pmb{S}}{\\sqrt{M-1}}$$\n",
" \n",
"\n",
"1. **E Step** (update posteriors). Find the expectation of the latent states given all the observational data ($E[\\pmb{z}_m|\\pmb{r}_m,\\pmb{\\theta}]$) and the expectation of the second moment of the latent states given the observational data ($E[\\pmb{z}_m\\pmb{z}_m'|\\pmb{r}_m],\\pmb{\\theta}$). Note: $\\pmb{\\theta}$ is all the latest update for parameters $\\pmb{L}$ and $\\epsilon$:\n",
"\n",
"$$E[\\pmb{z}_m|\\pmb{r}_m,\\pmb{\\theta}] = \\pmb{\\beta}(\\pmb{R}-\\mu)\\tag{26}$$\n",
" \n",
"$$E[\\pmb{z}_m\\pmb{z}_m'|\\pmb{r}_m,\\pmb{\\theta}] = Cov(\\pmb{z}_m|\\pmb{r}_m,\\pmb{\\theta}) + E[\\pmb{z}_m|\\pmb{r}_m,\\pmb{\\theta}]\\text{ }E[\\pmb{z}_m|\\pmb{r}_m,\\pmb{\\theta}]'$$\n",
"\n",
"$$= \\pmb{I}_N-\\pmb{\\beta} \\pmb{L} + \\pmb{\\beta}(\\pmb{r}_m-\\pmb{\\mu})\\text{ }(\\pmb{r}_m-\\pmb{\\mu})'\\pmb{\\beta}'\\tag{27}$$\n",
"\n",
"which result from using the posterior distribution mean and covariance (Equation 25). At this step, iterate and solve for all $M$ samples in the training partition.\n",
"\n",
"2. **M Step** (maximize likelihood). Find the parameters ($\\pmb{L}$ and $\\epsilon$) that maximize the likelihood for all the observational data (all time steps) given the latest update for the expected latent states (refer to Equation 24). This is done by taking the derivative, with respect to the parameters $\\pmb{L}$ and $\\pmb{\\Psi}$ (separately), for the log likelihood of all observational data (given the latent states) (Equation 28) and setting that derivative to 0 and solving for the respective parameter. Equation 28 makes use of the standard equation for a multivariate Gaussian density function. After taking the derivative of equation 28 with respect to the parameters of interest and setting that equal to 0, Equations 29 and 30 can be derived to give the parameter that maximizes the likelihood of the latent states given the observations.\n",
"\n",
"$$log(p(\\pmb{R}|\\pmb{Z})) = log(\\prod_{m=1}^{M}\\frac{exp(-\\frac{1}{2}(\\pmb{r}_m-\\pmb{\\mu}-\\pmb{L}\\pmb{z}_m)'\\pmb{\\Psi}^{-1}(\\pmb{r}_m-\\pmb{\\mu}-\\pmb{L}\\pmb{z}_m))}{\\sqrt{(2\\pi)^C |{\\pmb{\\Psi}}|}})\\tag{28}$$\n",
" \n",
"$$\\pmb{L}=(\\sum_{m=1}^M (\\pmb{r}_m-\\pmb{\\mu}) E[\\pmb{z}_m|\\pmb{r}_m]')(\\sum_{m=1}^M E[\\pmb{z}_m\\pmb{z}_m'|\\pmb{r}_m])^{-1}\\tag{29}$$\n",
"\n",
"$$\\pmb{\\Psi}=\\frac{1}{M}\\text{diag}[\\sum_{m=1}^M{(\\pmb{r}_m-\\mu) (\\pmb{r}_m-\\pmb{\\mu})'}-L E[\\pmb{z}_m|\\pmb{r}_m](\\pmb{r}_m-\\pmb{\\mu})']\\tag{30}$$\n",
"\n",
"For Equations 29 and 30, plug in the results for the expectations computed in the \"E\" step where appropriate. In equation 30, use the newest updated $\\pmb{L}$ parameter (output of Equation 29). Also, the \"diag()\" function in Equation 30 means to set all of the off-diagonal elements for the matrix computed inside the \"diag\" function to 0.\n",
"\n",
"3. **Check for Convergence Step**. Stop the EM algorithm if $100$ EM iterations has passed or if the Gaussian cross-entropy for the observations has decreased by $\\frac{1}{100}$ of the total decrease since initiating the EM algorithm:\n",
"\n",
" Cross-entropy for observational data can be found by taking the log of the product of Gaussian density functions for all samples in the $\\pmb{R}$ partition. $\\pmb{R} \\sim N(\\pmb{\\mu},\\pmb{L}\\pmb{L}' + \\pmb{\\Psi})$ (Note: $Cov[\\pmb{R},\\pmb{R}]$ is derived in the appendix as $\\pmb{L}\\pmb{L}' + \\pmb{\\Psi}$): \n",
"\n",
" $$log(\\prod_{m=1}^M p(\\pmb{r}_m)) = \\sum_{m=1}^M log(N(\\pmb{\\mu},\\pmb{L}\\pmb{L}' + \\pmb{\\Psi}))\\tag{31}$$"
]
},
{
"cell_type": "markdown",
"id": "31c1f6ca-da75-4bc0-a532-c6ee7b6d4a96",
"metadata": {},
"source": [
"##### Perform Expectation Maximization to Learn the Parameters with the Maximum Likelihood\n",
"\n",
"Following FA, an EM algorithm (different than FA's) can now be applied to learn the LGDS parameters for the model in Equations (20) and (21). The following steps are taken:\n",
"\n",
"0. **Initialization Step** (Initialize the parameters for the LGDS and the initial latent state and its covariance). The parameters, initial latent state, and initial latent covariance will be initialized as follows:\n",
"\n",
" $$\\pmb{Q}=\\pmb{\\Psi}$$\n",
"\n",
" This is initialized to the covariance of the likelihood function learned in FA (or $Cov[\\pmb{R}|\\pmb{Z}]$).\n",
"\n",
" $$\\pmb{W}=\\pmb{I}_N-\\pmb{\\beta} \\pmb{L}$$\n",
"\n",
" This is initialized to the covariance for the posterior distribution learned in FA (or $Cov[\\pmb{R}|\\pmb{Z}]$)\n",
"\n",
" $$\\pmb{H}=\\pmb{L}$$\n",
"\n",
" This is initialized to the factor loading matrix learned in FA, which transforms the latent states to the observational data in FA.\n",
"\n",
" $$\\pmb{A}=\\pmb{I_N}$$\n",
"\n",
" Here $\\pmb{A}$ is simply set to an identity matrix.\n",
"\n",
" $$\\pmb{\\hat{z}}^{-}_0=E[\\pmb{Z}|\\pmb{R}]$$\n",
"\n",
" This is initialized to the sample average of the expected latent states learned at each time step in FA.\n",
" \n",
" $$\\pmb{P}^{-}_0=Cov[\\pmb{Z}|\\pmb{R}]$$\n",
"\n",
" This is initialized to the sample covariance of the expected latent states learned at each time step in FA.\n",
"\n",
" $$\\pmb{a}=0$$\n",
"\n",
" Here the latent estimates will be assumed to have 0 mean as was assumed in FA.\n",
"\n",
" $$\\pmb{h}=0$$\n",
"\n",
" The observation offset vector will be assumed 0 and the mean will be removed from the observations, $\\pmb{R}$, as done in FA; however, after training, the mean of the observations will be accounted for in the model and factored back into the model as the $\\pmb{h}$ vector.\n",
" \n",
"\n",
"1. **E Step** (update posteriors). At the Expectation step, the approach by [Shumway and Stoffer 1982](https://www.semanticscholar.org/paper/AN-APPROACH-TO-TIME-SERIES-SMOOTHING-AND-USING-THE-Shumway-Stoffer/658ee89b35cde8dae323452f01146b6176b2ece8) is taken to compute the expected values for the latent state and their covariances. Those authors first perform a forward Kalman Filter recursion to compute posterior estimates for the latent states; then, they refine those estimates by working backwards to essentially smooth the data in light of future data. The smoothing backward recursion algorithm is known as the Rauch-Tung-Striebel Smoother. This can be outlined as follows: \n",
"\n",
" 1.1. First, apply the KF estimator as done in section 2.3 in the supervised case. In this case, the latent state estimates ($\\pmb{\\hat{z}_m}$), the uncertainty in the estimates ($\\pmb{P}_m$), and the prediction for the uncertainty of the estimates ($\\pmb{P}^{-}_m$) at each time step is retained. The final Kalman Gain ($\\pmb{K}$) computed in the KF will also be retained for use in the smoother to follow in this \"E\" step.\n",
" \n",
" 1.2. Second, apply the **Rauch-Tung-Striebel Smoother** to refine the KF estimates and compute an additional parameter needed for the \"M\" step to follow this \"E\" step. The KF estimates being refined are the expected latent states and their covariance at each time step. The additional parameter being computed here is the covariance matrix for the previous and current state, which describes the uncertainty in extrapolation estimate from one latent state to its next ($\\pmb{P}_{m,m-1}$). The Rauch-Tung-Striebel Smoother will iterate from the $M$-th sample to the first sample in the training partition and compute Equations 32-35 as follows:\n",
"\n",
" $$\\pmb{J}_{m-1}=\\pmb{P}_{m-1}\\pmb{A}'(\\pmb{P}_{m-1}^{-})^{-1}\\tag{32}$$\n",
"\n",
" where, $\\pmb{J}$ is the \"smoother gain matrix\".\n",
"\n",
" $$\\pmb{\\hat{z}}_{sm_{m-1}}=\\pmb{\\hat{z}}_{m-1} +\n",
" \\pmb{J}_{m-1}(\\pmb{\\hat{z}}_{sm_{m}} - \\pmb{A}\\pmb{\\hat{z}}_{m-1})\\tag{33}$$\n",
"\n",
" $$\\pmb{P}_{sm_{m-1}}=\\pmb{P}_{m-1} +\n",
" \\pmb{J}_{m-1}(\\pmb{P}_{sm_{m}} - \\pmb{P}_{m-1}^{-})\\pmb{J}_{m-1}'\\tag{34}$$\n",
"\n",
" where, $\\pmb{\\hat{z}_{sm}}$ and $\\pmb{P}_{sm}$ are the smoothed, refined KF latent state estimates and their covariance matrices, respectively ($\\pmb{\\hat{z}}$ and $\\pmb{P}$). Note: $\\pmb{\\hat{z}}_{sm_M}$ and $\\pmb{P}_{sm_M}$ are set to their respective KF estimates ($\\pmb{\\hat{z}_M}$ and $\\pmb{P}_M$).\n",
"\n",
" $$\\pmb{P}_{sm_{m-1,m-2}}=\\pmb{P}_{m-1}\\pmb{J}_{m-2}' +\n",
" \\pmb{J}_{m-1}(\\pmb{P}_{sm_{M,M-1}} - \\pmb{A}\\pmb{P}_{m-1})\\pmb{J}_{m-2}'\\tag{35}$$\n",
"\n",
" Note: Equation (35) will lag an iteration before it is first computed so that $\\pmb{J}_{m-2}$ is had. Though, at the first iteration the following will be computed (Note: $\\pmb{K_M}$ is the Kalman gain computed at the last time step from the KF algorithm run in 1.1.):\n",
"\n",
" $$\\pmb{P}_{sm_{M,M-1}}=(\\pmb{I}_N - \\pmb{K}_M\\pmb{H})\\pmb{A}\\pmb{P}_{m-1}$$\n",
"\n",
"\n",
" Following this backward recursion, the KF estimates, or the expected values for the latent states at each time step and their covariance, are then refined and $\\pmb{P_{m,m-1}}$ (at each time step) is computed in the following way:\n",
"\n",
" $$\\pmb{\\hat{z}}_m=\\pmb{\\hat{z}}_{sm_m}\\tag{36}$$\n",
"\n",
" $$\\pmb{P}_m=\\pmb{P}_{sm_m} + \\pmb{\\hat{z}}_{sm_m}\\pmb{\\hat{z}}_{sm_m}'\\tag{37}$$\n",
"\n",
" $$\\pmb{P}_{m,m-1}=\\pmb{P}_{sm_{M,M-1}} + \\pmb{\\hat{z}}_{sm_m}\\pmb{\\hat{z}}_{sm_{m-1}}'\\tag{38}$$\n",
"\n",
"3. **M Step** (maximize likelihood). This step finds all parameters that maximize the expected joint log likelihood for the latent and observed variables, given the observed data. This expected joint log likelihood is given by: \n",
"\n",
"$$E[log(p(\\pmb{Z},\\pmb{R})|\\pmb{R})]=E[log(p(\\pmb{z}_1)\\prod_{m=2}^M p(\\pmb{z}_m|\\pmb{z}_{m-1}) \\prod_{m=1}^M p(\\pmb{r}_m|\\pmb{z}_m))]\\tag{39}$$\n",
"\n",
"where,\n",
"\n",
"$p(\\pmb{z}_1) \\sim N(\\pmb{z}_0,\\pmb{P}_{0})$\n",
"\n",
"$p(\\pmb{z}_m|\\pmb{z}_{m-1}) \\sim N(\\pmb{A}\\pmb{z}_{m-1}\\text{, }\\pmb{W})$\n",
"\n",
"$p(\\pmb{r}_m|\\pmb{z}_m) \\sim N(\\pmb{H}\\pmb{z}_m\\text{, }\\pmb{Q})$.\n",
"\n",
"Here, $p(\\pmb{z}_m|\\pmb{z}_{m-1})$ and $p(\\pmb{r}_m|\\pmb{z}_m)$ are a result of the LGDS model. \n",
"\n",
"By differentiating Equation (32) with respect to the parameter of interest and then setting that equation to 0, the parameter that maximizes the likelihood can be solved for. The following equations are a result of doing that and are computed/updated on each pass of this \"M\" step:\n",
"\n",
"$$\\pmb{H}=(\\sum_{m=1}^M \\pmb{r}_m\\pmb{\\hat{z}}_m')(\\sum_{m=1}^M E[\\pmb{z}_m\\pmb{z}_m'|\\pmb{R}])^{-1}\\tag{40}$$\n",
"\n",
"$$\\pmb{Q}=\\frac{1}{M} \\sum_{m=1}^M (\\pmb{r}_m\\pmb{r}_m' - \\pmb{H}\\pmb{\\hat{z}}_m\\pmb{r}_m')\\tag{41}$$\n",
"\n",
"$$\\pmb{A}=(\\sum_{m=2}^M E[\\pmb{z}_m\\pmb{z}_{m-1}'|\\pmb{R}])(\\sum_{m=2}^M E[\\pmb{z}_{m-1}\\pmb{z}_{m-1}'|\\pmb{R}])^{-1}\\tag{42}$$\n",
"\n",
"$$\\pmb{W}=\\frac{1}{M-1} (\\sum_{m=2}^M E[\\pmb{z}_m\\pmb{z}_m'|\\pmb{R}] - \\pmb{A} \\sum_{m=2}^M E[\\pmb{z}_{m-1}\\pmb{z}_{m}'|\\pmb{R}])\\tag{43}$$\n",
"\n",
"$$\\pmb{z}_0=\\pmb{\\hat{z}}_1\\tag{44}$$\n",
"\n",
"$$\\pmb{P}_0=E[\\pmb{z}_{1} \\pmb{z}_{1}'|\\pmb{R}] -\n",
"\\pmb{\\bar{\\hat{z}}}_1 \\pmb{\\bar{\\hat{z}}}_1' +\n",
"\\frac{1}{N} \\sum_{n=1}^N (\\hat{z}_{n,1}-\\bar{\\hat{z}}_1)(\\hat{z}_{n,1}-\\bar{\\hat{z}}_1)'\\tag{45}$$\n",
"\n",
"Note: $\\pmb{\\bar{\\hat{z}}}_1$ is the sample average across all latent states at time step 1.\n",
"\n",
"3. **Check for Convergence Step**. If $100$ EM iterations were performed, training is complete (no more iterating between \"E\" and \"M\" here, move on). Otherwise, compare the average change in parameter elements from one \"M\" step to the next. If the max of those average parameter changes from one step to the next is $\\lt 0.005$, then stop the EM training here and move onto the estimation step (Section 3.3.)."
]
},
{
"cell_type": "markdown",
"id": "ca660b61-e850-4db1-9d04-deb11e10293a",
"metadata": {},
"source": [
"#### Estimation of Latent States\n",
"\n",
"The Kalman Filter unsupervised runs the same KF estimation algorithm as the supervised version (see section 2.3.). The difference here though is that the KF estimation algorithm no longer estimates for the kinematic state vector ($\\pmb{x}$) at each time step $m$, but instead estimates for a latent state vector, $\\pmb{z}_m$. Use the same KF estimation as in section 2.3., but plug in the LGDS parameters and initial latent states and uncertainty learned in section 3.2. above. After predicting the estimated latent state at each time step $m$, the prediction for the kinematic states is obtained as described in section 3.4. below. This is done on each sample in the \"evaluation\" partition for the session in an iterative manner."
]
},
{
"cell_type": "markdown",
"id": "c5e4ece3-71fe-47aa-8de7-f317f3443ffb",
"metadata": {},
"source": [
"#### Prediction of Kinematic State\n",
"\n",
"##### Static Mapping\n",
"\n",
"For the KF unsupervised with static mapping, the latent states are regressed onto the kinematic states via a linear static transformation:\n",
"\n",
"$$\\pmb{x}_m = \\pmb{D}\\pmb{z}_m + \\pmb{d}\\tag{33}$$\n",
"\n",
"where,\n",
"\n",
"$\\pmb{D} \\in (N,K)$ is the static matrix, which transforms the latent vector, $\\pmb{z}_m$, to the kinematic space.\n",
"\n",
"$\\pmb{d} \\in (N,1)$ is the static offset vector or mean for the kinematic states and scales up or down $\\pmb{D}\\pmb{z}_m$ to complete the transformation from $\\pmb{z}_m$ to $\\pmb{x}_m$.\n",
"\n",
"In this work, the static terms $\\pmb{D}$ and $\\pmb{d}$ are learned through a least squares approximation (equation 5) with the true kinematic training states utilized here. Although this is an \"unsupervised\" problem, performing an approximation this way assumes a best case of static mapping, or allows the researcher to gauge what performance can be expected if there is a good understanding for how the latent states map to the kinematic states beforehand. This is also how it is described as done in the [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) paper."
]
},
{
"cell_type": "markdown",
"id": "e4053ba1-954e-4929-81b8-c597e2d3fac5",
"metadata": {},
"source": [
"#### Run Kalman Filter Unsupervised with Static Mapping Implementation and Log Results:\n",
"\n",
"The first code snippet below will collect results for the main experiment, or the one that validates the implementation for the decoders (the SU results that are reported by [O'Doherty et al., 2020](https://zenodo.org/records/3854034)). The next code snippet collects the multi-unit results for the KF unsupervised with static mapping (or KF static) decoder, or that when the neural data input to the KF unsupervised model are the pooled electrode spikes (spike sorting abandoned). The last code snippet collects the results for the four spike dropped cases for the KF unsupervised with static mapping decoder ($5$ %, $15$ %, $25$ %, and $50$ % of spikes dropped)."
]
},
{
"cell_type": "markdown",
"id": "d9b58550-8b47-424d-b5d8-11451cf85e04",
"metadata": {},
"source": [
"##### Single-Unit KF Unsupervised with Static Mapping Results:"
]
},
{
"cell_type": "code",
"execution_count": 319,
"id": "6223ae3d-ea27-4b14-a404-a6ecc32007b6",
"metadata": {},
"outputs": [
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indy_20160407_02
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indy
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291
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20001
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31111
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vely
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0.554535
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3.511862
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2501
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15338
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posy
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128
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KF_static
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0.402945
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loco_20170302_02
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128
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KF_static
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0.422374
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2.383531
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1126
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loco_20170302_02
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loco
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500
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2501
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15338
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accx
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128
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KF_static
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0.019835
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0.087007
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"
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1127
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loco_20170302_02
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loco
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500
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2501
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15338
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accy
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128
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KF_static
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0.077881
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0.352132
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"
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" \n",
"
\n",
"
1128 rows × 10 columns
\n",
"
"
],
"text/plain": [
" session monkey num_neurons num_training_samples num_testing_samples kinematic_axis bin_width decoder rsq snr\n",
"0 indy_20160407_02 indy 291 20001 31111 posx 16 KF_static 0.434861 2.478451\n",
"1 indy_20160407_02 indy 291 20001 31111 posy 16 KF_static 0.546503 3.434257\n",
"2 indy_20160407_02 indy 291 20001 31111 velx 16 KF_static 0.477894 2.822414\n",
"3 indy_20160407_02 indy 291 20001 31111 vely 16 KF_static 0.554535 3.511862\n",
"4 indy_20160407_02 indy 291 20001 31111 accx 16 KF_static 0.120434 0.557316\n",
"... ... ... ... ... ... ... ... ... ... ...\n",
"1123 loco_20170302_02 loco 500 2501 15338 posy 128 KF_static 0.402945 2.239853\n",
"1124 loco_20170302_02 loco 500 2501 15338 velx 128 KF_static 0.166345 0.790135\n",
"1125 loco_20170302_02 loco 500 2501 15338 vely 128 KF_static 0.422374 2.383531\n",
"1126 loco_20170302_02 loco 500 2501 15338 accx 128 KF_static 0.019835 0.087007\n",
"1127 loco_20170302_02 loco 500 2501 15338 accy 128 KF_static 0.077881 0.352132\n",
"\n",
"[1128 rows x 10 columns]"
]
},
"execution_count": 319,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resultsDir = os.getcwd() + r\"\\single_unit_results\"\n",
"df_kfStatic = mt.collectResults(decoder=\"KF_static\", dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDir,\\\n",
" bMU=False, dropPercent=0,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=0,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"df_kfStatic"
]
},
{
"cell_type": "markdown",
"id": "753cb9e7-62ea-4e71-88ad-86d2a30e9cce",
"metadata": {},
"source": [
"##### Multi-Unit KF Unsupervised with Static Mapping Results:"
]
},
{
"cell_type": "code",
"execution_count": 320,
"id": "c200da56-6815-4189-9b89-62f4e2ea98d6",
"metadata": {},
"outputs": [],
"source": [
"resultsDirMU = os.getcwd() + r\"\\multi_unit_results\"\n",
"df_kfStaticMU = mt.collectResults(decoder=\"KF_static\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirMU,\\\n",
" bMU=True, dropPercent=0,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=0,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)"
]
},
{
"cell_type": "markdown",
"id": "ea945c26-6ad9-4d00-9275-08d4207dab76",
"metadata": {},
"source": [
"##### 3.5.3. Spike Dropped KF Unsupervised with Static Mapping Results:"
]
},
{
"cell_type": "code",
"execution_count": 736,
"id": "d5576eaf-dcb8-4e22-bc3e-d0b0a0fb900d",
"metadata": {},
"outputs": [],
"source": [
"resultsDirDrop = os.getcwd() + r\"\\dropped_spikes_results\"\n",
"df_kfStaticD05 = mt.collectResults(decoder=\"KF_static\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=5,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_kfStaticD15 = mt.collectResults(decoder=\"KF_static\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=15,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_kfStaticD25 = mt.collectResults(decoder=\"KF_static\",\\\n",
" dataDir=os.getcwd(),\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=25,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)\n",
"\n",
"df_kfStaticD50 = mt.collectResults(decoder=\"KF_static\",\\\n",
" dataDir=os.getcwd(),\\\n",
" resultsDir=resultsDirDrop,\\\n",
" bMU=False, dropPercent=50,\\\n",
" bTransferLearn=False,\\\n",
" bUseOldResults=True,\\\n",
" bNewFile=True, fExt=1,\\\n",
" bPrintRes=False,\\\n",
" bSaveParams=False)"
]
},
{
"cell_type": "markdown",
"id": "9438d216-2409-437d-b6d3-7bdeb711ff41",
"metadata": {},
"source": [
"# Results\n",
"The [O'Doherty et al., 2020](https://zenodo.org/records/3854034) dataset comes with a results file, which has results for all sessions, kinematic axes, and bin type for all of the 7 decoders tested (regression, KF Observed, KF Unobserved with Static mapping, KF Unobserved with Dynamic mapping, Unscented KF, rEFH with Static mapping, and rEFH with Dyanamic mapping) from the [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) effort. The table output, or dataframe, directly below shows a snippet of these results. In order to make comparison to the [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) results, the *custom makin_2018_tools* library is designed to return and operate on results in that same format. Hence, why the results dataframes for the different decoders shown in the **Methodology** section above are formatted similarly."
]
},
{
"cell_type": "markdown",
"id": "08e4fd0d",
"metadata": {},
"source": [
"## Makin Results"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "bee77ffb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
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session
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monkey
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num_neurons
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num_training_samples
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num_testing_samples
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kinematic_axis
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bin_width
\n",
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decoder
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rsq
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snr
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0
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indy_20160407_02
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indy
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291
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320
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31111
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posx
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16
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regression
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0.073015
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0.329274
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"
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"
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1
\n",
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indy_20160407_02
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indy
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291
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320
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31111
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posx
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16
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KF_observed
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0.658503
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4.666132
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2
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indy_20160407_02
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16
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KF_static
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0.132615
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0.617882
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3
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indy_20160407_02
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320
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31111
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posx
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16
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KF_dynamic
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0.467168
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2.734097
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4
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indy_20160407_02
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320
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31111
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posx
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16
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UKF
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0.735999
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5.783946
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128
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0.044782
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7894
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320
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8828
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accy
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128
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rEFH_static
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0.401935
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2.232513
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"
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7895
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loco_20170302_02
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loco
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500
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320
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accy
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128
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rEFH_dynamic
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0.423537
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2.392283
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"
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" \n",
"
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"
7896 rows × 10 columns
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"
"
],
"text/plain": [
" session monkey num_neurons num_training_samples \\\n",
"0 indy_20160407_02 indy 291 320 \n",
"1 indy_20160407_02 indy 291 320 \n",
"2 indy_20160407_02 indy 291 320 \n",
"3 indy_20160407_02 indy 291 320 \n",
"4 indy_20160407_02 indy 291 320 \n",
"... ... ... ... ... \n",
"7891 loco_20170302_02 loco 500 320 \n",
"7892 loco_20170302_02 loco 500 320 \n",
"7893 loco_20170302_02 loco 500 320 \n",
"7894 loco_20170302_02 loco 500 320 \n",
"7895 loco_20170302_02 loco 500 320 \n",
"\n",
" num_testing_samples kinematic_axis bin_width decoder rsq \\\n",
"0 31111 posx 16 regression 0.073015 \n",
"1 31111 posx 16 KF_observed 0.658503 \n",
"2 31111 posx 16 KF_static 0.132615 \n",
"3 31111 posx 16 KF_dynamic 0.467168 \n",
"4 31111 posx 16 UKF 0.735999 \n",
"... ... ... ... ... ... \n",
"7891 8828 accy 128 KF_static 0.073140 \n",
"7892 8828 accy 128 KF_dynamic 0.129986 \n",
"7893 8828 accy 128 UKF 0.044782 \n",
"7894 8828 accy 128 rEFH_static 0.401935 \n",
"7895 8828 accy 128 rEFH_dynamic 0.423537 \n",
"\n",
" snr \n",
"0 0.329274 \n",
"1 4.666132 \n",
"2 0.617882 \n",
"3 2.734097 \n",
"4 5.783946 \n",
"... ... \n",
"7891 0.329861 \n",
"7892 0.604736 \n",
"7893 0.198973 \n",
"7894 2.232513 \n",
"7895 2.392283 \n",
"\n",
"[7896 rows x 10 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f_results = r\"../refh_results.csv\"\n",
"\n",
"df_makin = pd.read_csv(f_results)\n",
"df_makin"
]
},
{
"cell_type": "markdown",
"id": "c1b7c6d4-a57d-4e42-bcea-61950049135d",
"metadata": {},
"source": [
"## Single-Unit/[Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) Reproduction Results\n",
"\n",
"The main goal for this project was to develop a Python library for signal decoding. As a way to validate the implementation of neural decoders, the results from [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) can be leveraged and compared for exactness to the results found on the custom implementations.\n",
"\n",
"### Qualitative Assesment for Decoder Implementation\n",
"\n",
"As a qualitative assessment for implementation, the SNR results for the single-unit data reported by [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) and in this work for the different sessions, decoders, bin widths, and kinematic states can be plotted together. Specifically, a scatter plot can be plotted where each data point has coordinates ([Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) SNR results, this Project SNR Results) for corresponding session, binwidth, kinematic state, and monkey for each decoder. The code below creates this plot."
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "58f8c044-5f1e-41c7-8b6b-bba2b02ce562",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# neural signal decoders implemented in this project\n",
"decoders = [\"regression\", \"KF_observed\", \"KF_static\"]\n",
"\n",
"# plot for each decoder, a scatter plot distinguished by\n",
"# monkey, bin width, and kinematic axis (hence the 3, below)\n",
"f, ax = plt.subplots(len(decoders),3, sharex=True, sharey=True, figsize=(10,10))\n",
"\n",
"# set the axes ranges for the plots (snr)\n",
"lowerR = -4\n",
"upperR = 8\n",
"for i,j in enumerate(decoders):\n",
" if (j == \"regression\"):\n",
" df_merge = mt.mergeResults(df_makin,df_regress)\n",
" elif (j == \"KF_observed\"):\n",
" df_merge = mt.mergeResults(df_makin,df_kfObs)\n",
" elif (j == \"KF_static\"):\n",
" df_merge = mt.mergeResults(df_makin,df_kfStatic)\n",
"\n",
" # three distinguishable criteria for decoding on sessions\n",
" for k in range(3):\n",
" if (k == 0):\n",
" # get the unique monkeys\n",
" filt_criteria=\"monkey\"\n",
" elif (k == 1):\n",
" # get the unique kinematic axes\n",
" filt_criteria=\"kinematic_axis\"\n",
" elif (k == 2):\n",
" # get the unique bin widths\n",
" filt_criteria=\"bin_width\"\n",
" \n",
" # get the unique objects for filter criteria\n",
" filter = np.unique(df_merge[filt_criteria])\n",
"\n",
" # plot by filter criteria\n",
" for l,m in enumerate(filter):\n",
" iFilt = np.where(df_merge[filt_criteria] == m)[0]\n",
" ax[i][k].plot(df_merge[\"snr_ref\"].iloc[iFilt],\\\n",
" df_merge[\"snr\"].iloc[iFilt], '.',\\\n",
" ms=2, label=m)\n",
"\n",
" # plot a 1 for 1 reference line\n",
" # (if results coordinates land on this, they match)\n",
" refLine = np.linspace(lowerR,upperR)\n",
" ax[i][k].plot(refLine, refLine, '--', lw=2, color=\"black\")\n",
"\n",
" # place axis and title labels\n",
" if (i == 0):\n",
" ax[i][k].set_title(\"SNR Comparison by\\n\" + filt_criteria)\n",
"\n",
" if (k == 0):\n",
" ax[i][k].set_ylabel(j + '\\n\\nCustom SNR')\n",
"\n",
" if (i == len(decoders)-1):\n",
" ax[i][k].set_xlabel(\"Makin SNR\")\n",
"\n",
" # configure plot axes\n",
" ax[i][k].set_xlim(lowerR, upperR)\n",
" ax[i][k].set_ylim(lowerR, upperR)\n",
" ax[i][k].set_aspect('equal', adjustable='box')\n",
" ax[i][k].legend(frameon=False,markerscale=4)\n",
" ax[i][k].grid()\n",
"\n",
"# adjust plots to fit tightly\n",
"f.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "895eb76b-d085-44a6-b720-b2053c06300b",
"metadata": {},
"source": [
"*Figure 4. This plot provides a qualitative assessment for how well the custom implemented decoders match the results for [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) on the same dataset.*\n",
"\n",
"Figure 4 shows a couple points: one, this project was not able to replicate the results for the \"loco\" monkey and get them to match the baseline [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) results (for this reason, only \"indy\" sessions will be considered going forward); two, the KF unsupervised with static mapping implemented in this project appears to outperform the baseline implementation.\n",
"\n",
"### Bootstrapping Validation\n",
"\n",
"To quantify the similarity between the regression and KF supervised implementations and by how much the custom KF unsupervised decoder outperforms the baseline implementation, bootstrapping will be performed.\n",
"\n",
"In [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) bootstrapping was performed by re-sampling (with replacement) a new set of SNRs from the original results for all sessions 100,000 times. Each time, the SNR average (or average difference, for comparisons), weighted by the number of samples in a session's evaluation partition, was computed. This returns a distribution of 100,000 SNRs and allows for statistical inference (e.g. stating a statistic with a confidence level). In this project's custom library, a bootstrapping function was made to do this (*bootstrapPrimateDat*). As a test for how well the bootstrapping implementation works, the average SNR performance for [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) main experiment's baseline decoder (regression) is computed. This result was reported in Figure 2 of that paper. This comparison is made in the code below:"
]
},
{
"cell_type": "code",
"execution_count": 144,
"id": "8cf26d92-1c72-4d8c-920b-af9758a4977f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decoder |Kinematic State|Bin Width (ms)|Makin Reported SNR Avg |Computed SNR Avg |% Diff SNR Avg \n",
"Regression |posx | 64|0.6300 dB |0.6238 dB |0.99 % \n",
"Regression |posy | 64|1.5100 dB |1.5336 dB |1.55 % \n",
"Regression |velx | 64|1.0600 dB |1.0540 dB |0.56 % \n",
"Regression |vely | 64|1.8100 dB |1.8268 dB |0.92 % \n",
"Regression |accx | 64|0.0900 dB |0.0889 dB |1.24 % \n",
"Regression |accy | 64|0.3000 dB |0.2967 dB |1.09 % \n"
]
}
],
"source": [
"kinAx = [\"posx\",\"posy\",\"velx\",\"vely\",\"accx\",\"accy\"]\n",
"\n",
"# the main experiment in Makin et al., 2018\n",
"# compared results for a 64 ms bin size\n",
"binWidth = [64] \n",
"\n",
"# these are the reported results corresponding\n",
"# to the kinematic axes in kinAx, respectively\n",
"reportedSNR = [0.63,1.51,1.06,1.81,0.09,0.3]\n",
"\n",
"# form print template for results\n",
"printTemp = \"{0:12}|{1:15}|{2:14}|{3:30}|{4:25}|{5:25}\"\n",
"# headers\n",
"print(printTemp.format(\"Decoder\",\\\n",
" \"Kinematic State\",\\\n",
" \"Bin Width (ms)\",\\\n",
" \"Makin Reported SNR Avg\",\\\n",
" \"Computed SNR Avg\",\\\n",
" \"% Diff SNR Avg\"\n",
" ))\n",
"\n",
"# compute the bootstrap average for each kinematic axis,\n",
"# binned at 64 ms for the regression decoder\n",
"for k,i in enumerate(kinAx):\n",
" for _,j in enumerate(binWidth):\n",
" res_mk = mt.bootstrapPrimateDat(dfRef=df_makin ,\\\n",
" statistic=\"mean\",\\\n",
" decoder=\"regression\",\\\n",
" monkey=\"combined\",\\\n",
" bin_width=j, kinAx=i)\n",
"\n",
" # compute average from the 100,000 weighted bootstrap avgs\n",
" mk_snr_avg = np.average(res_mk.bootstrap_distribution)\n",
"\n",
" # compute the percent difference in the average from that reported\n",
" pDiffAvg = np.abs((mk_snr_avg-reportedSNR[k])\\\n",
" /np.average([mk_snr_avg, reportedSNR[k]]))*100\n",
"\n",
" # print the results with the print template\n",
" print(printTemp.format(\"Regression\",\\\n",
" i,\\\n",
" j,\\\n",
" f\"{reportedSNR[k]:.4f} dB\",\\\n",
" f\"{mk_snr_avg:.4f} dB\",\\\n",
" f\"{pDiffAvg:.2f} %\"\n",
" ))"
]
},
{
"cell_type": "markdown",
"id": "3dff7f6c-b8d9-4520-afd3-8c2d3da1e7b9",
"metadata": {},
"source": [
"As can be seen from the results above, the difference in average SNR's computed in this effort from that reported in [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) is less than 2 % for all kinematic axes. Note: there are 2 result files missing from the dataset for another monkey which is not in the [O'Doherty et al., 2020](https://zenodo.org/records/3854034) dataset and could explain the slight difference here.\n",
"\n",
"In the next two code cells below, regression and KF supervised bootstrap results are collected for all bin width and kinematic axis permutations, respectively. The output of the code cells is a table showing the percent difference in average SNR and standard error for the bootstrap distributions between this project and [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95). Also, for the 64 ms bin width case, a plot follows each table overlaying the average SNR bootstrap distributions for each kinematic axis.\n",
"\n",
"### Regression Decoder Implementation Validation"
]
},
{
"cell_type": "code",
"execution_count": 731,
"id": "7c25c287-1b8c-445b-9c93-509ec185a436",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"kinAx = [\"combined\",\"posx\",\"posy\",\"velx\",\"vely\",\"accx\",\"accy\"]\n",
"binWidth = [\"combined\",16,32,64,128]\n",
"\n",
"# plot the 64 ms bin width SNR Average distributions\n",
"# between Makin and this Project \n",
"fig, ax = plt.subplots(len(kinAx),1, figsize=(12,15))\n",
"maxX = 0\n",
"minX = 0\n",
"\n",
"# loop for each kinematic axis and bin width and compute bootstrap statistics\n",
"statData = []\n",
"for k,i in enumerate(kinAx):\n",
" for _,j in enumerate(binWidth):\n",
" # bootstrap and collect avgs for Makin's (mk)\n",
" # and this projects results (ms)\n",
" res_mk = mt.bootstrapPrimateDat(dfRef=df_makin,\\\n",
" statistic=\"mean\",\\\n",
" decoder=\"regression\",\\\n",
" monkey=\"indy\",\\\n",
" bin_width=j, kinAx=i)\n",
" res_ms = mt.bootstrapPrimateDat(dfRef=df_regress,\\\n",
" statistic=\"mean\",\\\n",
" decoder=\"regression\",\\\n",
" monkey=\"indy\",\\\n",
" bin_width=j, kinAx=i)\n",
"\n",
" # compute sample average from bootstrap distributions \n",
" mk_snr_avg = np.average(res_mk.bootstrap_distribution)\n",
" ms_snr_avg = np.average(res_ms.bootstrap_distribution)\n",
"\n",
" # compute percent difference in mk and ms computed average\n",
" pDiffAvg = np.abs((mk_snr_avg-ms_snr_avg)\\\n",
" /np.average([mk_snr_avg, ms_snr_avg]))*100\n",
"\n",
" # compute percent difference in standard error for mk and ms\n",
" pDiffStdE= np.abs((res_mk.standard_error-\\\n",
" res_ms.standard_error)\\\n",
" /np.average([res_mk.standard_error,\\\n",
" res_ms.standard_error]))*100\n",
"\n",
" # collect statistical results for printing\n",
" statData.append([ \"regress\",\\\n",
" i,\\\n",
" j,\\\n",
" f\"{mk_snr_avg:8.4f} dB\",\\\n",
" f\"{ms_snr_avg:8.4f} dB\",\\\n",
" f\"{pDiffAvg:6.2f} %\",\\\n",
" f\"{res_mk.standard_error:8.4f} dB\",\\\n",
" f\"{res_ms.standard_error:8.4f} dB\",\\\n",
" f\"{pDiffStdE:6.2f} %\"])\n",
"\n",
" # plot the 64 ms binned bootstrap SNR avg distributions\n",
" if (j == 64):\n",
" \n",
" if (len(kinAx) <= 1):\n",
" pAx = ax\n",
" else:\n",
" pAx = ax[k]\n",
" \n",
" h1 = pAx.hist(res_mk.bootstrap_distribution,\\\n",
" bins=50, color=\"green\",\\\n",
" edgecolor=\"black\",\\\n",
" label=\"Makin Avg SNR\")\n",
" h2 = pAx.hist(res_ms.bootstrap_distribution,\\\n",
" bins=50, color=\"blue\",\\\n",
" edgecolor=\"black\", alpha=0.4,\\\n",
" label=\"Samarco Avg SNR\")\n",
"\n",
" # retain mins and max's for lims\n",
" maxX = max([maxX, max(h1[1]), max(h2[1])])\n",
" minX = min([minX, min(h1[1]), min(h2[1])])\n",
" pAx.set_title('Regression Results (' + f\"{i})\")\n",
" if (k == (len(kinAx)-1)):\n",
" pAx.set_xlabel('SNR (dB)')\n",
" pAx.set_ylabel('frequency')\n",
" \n",
" pAx.grid()\n",
" pAx.legend()\n",
"\n",
"for i in ax:\n",
" i.set_xlim([minX,maxX])\n",
" i.set_xticks(np.arange(minX,maxX,0.1))\n",
"\n",
"plt.subplots_adjust(hspace = 0.4)\n",
"plt.show()\n",
"\n",
"# put statistical results in a df \n",
"dfReg_res = pd.DataFrame(statData, columns=[ \"Decoder\",\\\n",
" \"KinState\",\\\n",
" \"Bin (ms)\",\\\n",
" \"Makin SNR Avg.\",\\\n",
" \"Samarco SNR Avg\",\\\n",
" \"% Diff SNR Avg\",\\\n",
" \"Makin SNR Std Err\",\\\n",
" \"MS SNR Std Err\",\\\n",
" \"% Diff Std Err\"])"
]
},
{
"cell_type": "markdown",
"id": "d89fcc57-3367-4a5f-b281-675eeb4f8ef9",
"metadata": {},
"source": [
"*Figure 5. This plot shows a comparison for the avg SNR bootstrap distributions for [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) and this Project's results (Samarco) on the same dataset for the regression decoder. For mostly all axes, the two distributions align very nicely. The average SNR acceleration distributions look to have similar standard error but differ somewhat in overall average.*"
]
},
{
"cell_type": "code",
"execution_count": 732,
"id": "5615fb2d-af93-48ac-a59c-a482c39e7a7f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Decoder
\n",
"
KinState
\n",
"
Bin (ms)
\n",
"
Makin SNR Avg.
\n",
"
Samarco SNR Avg
\n",
"
% Diff SNR Avg
\n",
"
Makin SNR Std Err
\n",
"
MS SNR Std Err
\n",
"
% Diff Std Err
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
regress
\n",
"
combined
\n",
"
combined
\n",
"
0.6985 dB
\n",
"
0.6892 dB
\n",
"
1.33 %
\n",
"
0.0371 dB
\n",
"
0.0372 dB
\n",
"
0.35 %
\n",
"
\n",
"
\n",
"
5
\n",
"
regress
\n",
"
posx
\n",
"
combined
\n",
"
0.4825 dB
\n",
"
0.4863 dB
\n",
"
0.79 %
\n",
"
0.0548 dB
\n",
"
0.0554 dB
\n",
"
1.19 %
\n",
"
\n",
"
\n",
"
10
\n",
"
regress
\n",
"
posy
\n",
"
combined
\n",
"
0.9992 dB
\n",
"
1.0070 dB
\n",
"
0.78 %
\n",
"
0.0924 dB
\n",
"
0.0913 dB
\n",
"
1.16 %
\n",
"
\n",
"
\n",
"
15
\n",
"
regress
\n",
"
velx
\n",
"
combined
\n",
"
0.9180 dB
\n",
"
0.9117 dB
\n",
"
0.68 %
\n",
"
0.0646 dB
\n",
"
0.0627 dB
\n",
"
2.88 %
\n",
"
\n",
"
\n",
"
20
\n",
"
regress
\n",
"
vely
\n",
"
combined
\n",
"
1.3349 dB
\n",
"
1.3138 dB
\n",
"
1.59 %
\n",
"
0.0992 dB
\n",
"
0.0977 dB
\n",
"
1.54 %
\n",
"
\n",
"
\n",
"
25
\n",
"
regress
\n",
"
accx
\n",
"
combined
\n",
"
0.1808 dB
\n",
"
0.1606 dB
\n",
"
11.87 %
\n",
"
0.0169 dB
\n",
"
0.0157 dB
\n",
"
7.34 %
\n",
"
\n",
"
\n",
"
30
\n",
"
regress
\n",
"
accy
\n",
"
combined
\n",
"
0.2925 dB
\n",
"
0.2722 dB
\n",
"
7.20 %
\n",
"
0.0221 dB
\n",
"
0.0210 dB
\n",
"
5.23 %
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Decoder KinState Bin (ms) Makin SNR Avg. Samarco SNR Avg % Diff SNR Avg Makin SNR Std Err MS SNR Std Err % Diff Std Err\n",
"0 regress combined combined 0.6985 dB 0.6892 dB 1.33 % 0.0371 dB 0.0372 dB 0.35 %\n",
"5 regress posx combined 0.4825 dB 0.4863 dB 0.79 % 0.0548 dB 0.0554 dB 1.19 %\n",
"10 regress posy combined 0.9992 dB 1.0070 dB 0.78 % 0.0924 dB 0.0913 dB 1.16 %\n",
"15 regress velx combined 0.9180 dB 0.9117 dB 0.68 % 0.0646 dB 0.0627 dB 2.88 %\n",
"20 regress vely combined 1.3349 dB 1.3138 dB 1.59 % 0.0992 dB 0.0977 dB 1.54 %\n",
"25 regress accx combined 0.1808 dB 0.1606 dB 11.87 % 0.0169 dB 0.0157 dB 7.34 %\n",
"30 regress accy combined 0.2925 dB 0.2722 dB 7.20 % 0.0221 dB 0.0210 dB 5.23 %"
]
},
"execution_count": 732,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i_combo = np.where(dfReg_res[\"Bin (ms)\"] == \"combined\")[0]\n",
"dfReg_res.iloc[i_combo]"
]
},
{
"cell_type": "markdown",
"id": "058c840e-5ebf-4194-889e-e182eb5d6897",
"metadata": {},
"source": [
"As evident in the table and plots above for the regression decoder, the implementation in this project matches its reference fairly well (<1% difference in SNR average performance for all kinematic states, aside from acceleration, when considering all bin width results). Acceleration predictions are least accurate with 7-12% difference in SNR average performance, suggesting a difference in implementation here for acceleration computation."
]
},
{
"cell_type": "markdown",
"id": "98dce1e4-7e4d-48ad-8eef-77148dd3ec76",
"metadata": {},
"source": [
"### KF Supervised Decoder Implementation Validation"
]
},
{
"cell_type": "code",
"execution_count": 721,
"id": "d0c4aaaf-f9ca-4908-997d-c8c3fa08a4f4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"kinAx = [\"combined\",\"posx\",\"posy\",\"velx\",\"vely\",\"accx\",\"accy\"]\n",
"binWidth = [\"combined\",16,32,64,128]\n",
"\n",
"# plot the 64 ms bin width SNR Average distributions\n",
"# between Makin and this Project \n",
"fig, ax = plt.subplots(len(kinAx),1, figsize=(12,15))\n",
"maxX = 0\n",
"minX = 0\n",
"\n",
"# loop for each kinematic axis and bin width and compute bootstrap statistics\n",
"statData = []\n",
"for k,i in enumerate(kinAx):\n",
" for _,j in enumerate(binWidth):\n",
" # bootstrap and collect avgs for Makin's (mk)\n",
" # and this projects results (ms)\n",
" res_mk = mt.bootstrapPrimateDat(dfRef=df_makin,\\\n",
" statistic=\"mean\",\\\n",
" decoder=\"KF_observed\",\\\n",
" monkey=\"indy\",\\\n",
" bin_width=j, kinAx=i)\n",
" res_ms = mt.bootstrapPrimateDat(dfRef=df_kfObs,\\\n",
" statistic=\"mean\",\\\n",
" decoder=\"KF_observed\",\\\n",
" monkey=\"indy\",\\\n",
" bin_width=j, kinAx=i)\n",
"\n",
" # compute sample average from bootstrap distributions\n",
" mk_snr_avg = np.average(res_mk.bootstrap_distribution)\n",
" ms_snr_avg = np.average(res_ms.bootstrap_distribution)\n",
"\n",
" # compute percent difference in mk and ms computed average\n",
" pDiffAvg = np.abs((mk_snr_avg-ms_snr_avg)\\\n",
" /np.average([mk_snr_avg, ms_snr_avg]))*100\n",
"\n",
" # compute percent difference in standard error for mk and ms\n",
" pDiffStdE= np.abs((res_mk.standard_error-\\\n",
" res_ms.standard_error)\\\n",
" /np.average([res_mk.standard_error,\\\n",
" res_ms.standard_error]))*100\n",
"\n",
" # collect statistical results for printing\n",
" statData.append([ \"KFObs\",\\\n",
" i,\\\n",
" j,\\\n",
" f\"{mk_snr_avg:8.4f} dB\",\\\n",
" f\"{ms_snr_avg:8.4f} dB\",\\\n",
" f\"{pDiffAvg:6.2f} %\",\\\n",
" f\"{res_mk.standard_error:8.4f} dB\",\\\n",
" f\"{res_ms.standard_error:8.4f} dB\",\\\n",
" f\"{pDiffStdE:6.2f} %\"])\n",
"\n",
" # plot the 64 ms binned bootstrap SNR avg distributions\n",
" if (j == 64):\n",
" if (len(kinAx) <= 1):\n",
" pAx = ax\n",
" else:\n",
" pAx = ax[k]\n",
" h1 = pAx.hist(res_mk.bootstrap_distribution,\\\n",
" bins=50, color=\"green\",\\\n",
" edgecolor=\"black\", label=\"Makin Avg SNR\")\n",
" h2 = pAx.hist(res_ms.bootstrap_distribution,\\\n",
" bins=50, color=\"blue\",\\\n",
" edgecolor=\"black\", alpha=0.4,\\\n",
" label=\"Samarco Avg SNR\")\n",
" maxX = max([maxX, max(h1[1]), max(h2[1])])\n",
" minX = min([minX, min(h1[1]), min(h2[1])])\n",
" pAx.set_title('KF Observed Results (' + f\"{i})\")\n",
" if (k == (len(kinAx)-1)):\n",
" pAx.set_xlabel('SNR (dB)')\n",
" pAx.set_ylabel('frequency')\n",
" \n",
" pAx.grid()\n",
" pAx.legend()\n",
"\n",
"for i in ax:\n",
" i.set_xlim([minX,maxX])\n",
" i.set_xticks(np.arange(minX,maxX,0.2))\n",
"\n",
"plt.subplots_adjust(hspace = 0.4)\n",
"plt.show()\n",
"\n",
"# put statistical results in a df \n",
"dfKfObs_res = pd.DataFrame(statData, columns=[ \"Decoder\",\\\n",
" \"KinState\",\\\n",
" \"Bin (ms)\",\\\n",
" \"Makin SNR Avg.\",\\\n",
" \"Samarco SNR Avg\",\\\n",
" \"% Diff SNR Avg\",\\\n",
" \"Makin SNR Std Err\",\\\n",
" \"MS SNR Std Err\",\\\n",
" \"% Diff Std Err\"])"
]
},
{
"cell_type": "markdown",
"id": "18fd4b5f-68a7-4666-b8e3-2e6b9ef5f9de",
"metadata": {},
"source": [
"*Figure 6. This plot shows a comparison for the avg SNR bootstrap distributions for [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95) and this Project's results (Samarco) on the same dataset for the KF supervised decoder. All distributions, aside acceleration, agree/align very nicely.*"
]
},
{
"cell_type": "code",
"execution_count": 728,
"id": "9c4e1a31-09b8-46aa-ae23-af525c80d3d6",
"metadata": {},
"outputs": [
{
"data": {
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Decoder
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KinState
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Bin (ms)
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Makin SNR Avg.
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Samarco SNR Avg
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% Diff SNR Avg
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Makin SNR Std Err
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MS SNR Std Err
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% Diff Std Err
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0
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KFObs
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combined
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combined
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2.4480 dB
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2.3986 dB
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2.04 %
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0.1204 dB
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0.1194 dB
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0.81 %
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5
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KFObs
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posx
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combined
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3.5675 dB
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3.5237 dB
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1.24 %
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0.2406 dB
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0.2336 dB
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2.99 %
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10
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KFObs
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posy
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combined
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4.4497 dB
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4.3943 dB
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1.25 %
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0.2035 dB
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0.2158 dB
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5.83 %
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15
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KFObs
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velx
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combined
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2.4713 dB
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2.4334 dB
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1.55 %
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0.1012 dB
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"
0.0990 dB
\n",
"
2.16 %
\n",
"
\n",
"
\n",
"
20
\n",
"
KFObs
\n",
"
vely
\n",
"
combined
\n",
"
3.3069 dB
\n",
"
3.2312 dB
\n",
"
2.32 %
\n",
"
0.1471 dB
\n",
"
0.1416 dB
\n",
"
3.77 %
\n",
"
\n",
"
\n",
"
25
\n",
"
KFObs
\n",
"
accx
\n",
"
combined
\n",
"
0.3260 dB
\n",
"
0.2930 dB
\n",
"
10.66 %
\n",
"
0.0607 dB
\n",
"
0.0549 dB
\n",
"
10.09 %
\n",
"
\n",
"
\n",
"
30
\n",
"
KFObs
\n",
"
accy
\n",
"
combined
\n",
"
0.5937 dB
\n",
"
0.5428 dB
\n",
"
8.95 %
\n",
"
0.0631 dB
\n",
"
0.0626 dB
\n",
"
0.76 %
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Decoder KinState Bin (ms) Makin SNR Avg. Samarco SNR Avg % Diff SNR Avg Makin SNR Std Err MS SNR Std Err % Diff Std Err\n",
"0 KFObs combined combined 2.4480 dB 2.3986 dB 2.04 % 0.1204 dB 0.1194 dB 0.81 %\n",
"5 KFObs posx combined 3.5675 dB 3.5237 dB 1.24 % 0.2406 dB 0.2336 dB 2.99 %\n",
"10 KFObs posy combined 4.4497 dB 4.3943 dB 1.25 % 0.2035 dB 0.2158 dB 5.83 %\n",
"15 KFObs velx combined 2.4713 dB 2.4334 dB 1.55 % 0.1012 dB 0.0990 dB 2.16 %\n",
"20 KFObs vely combined 3.3069 dB 3.2312 dB 2.32 % 0.1471 dB 0.1416 dB 3.77 %\n",
"25 KFObs accx combined 0.3260 dB 0.2930 dB 10.66 % 0.0607 dB 0.0549 dB 10.09 %\n",
"30 KFObs accy combined 0.5937 dB 0.5428 dB 8.95 % 0.0631 dB 0.0626 dB 0.76 %"
]
},
"execution_count": 728,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i_combo = np.where(dfKfObs_res[\"Bin (ms)\"] == \"combined\")[0]\n",
"dfKfObs_res.iloc[i_combo]"
]
},
{
"cell_type": "markdown",
"id": "3c34ba8b-937f-4e62-b7ee-f6b46feadf69",
"metadata": {},
"source": [
"As evident in the table and plots above for the KF supervised decoder the implementation in this project matches its reference fairly well (~2 % difference in average SNR for the decoders when considering all bin widths and kinematic states). Acceleration predictions are least accurate with 9-11% difference in SNR average performance, suggesting a difference in implementation here for acceleration computation."
]
},
{
"cell_type": "markdown",
"id": "e34a08df-c483-4f81-9db8-251ec458f21d",
"metadata": {},
"source": [
"### KF Unsupervised Decoder Implementation Validation\n",
"\n",
"Now, as evident in figure 4, the decoder implemented here for the KF Unsupervised with static mapping performs better than the reference implementation by [Makin et al., 2018](https://iopscience.iop.org/article/10.1088/1741-2552/aa9e95). What is left is to quantify by how much the expected performance improvement is for this project's implementation. The code below will collect bootstrap results for average SNR performances between the two KF unsupervised with static mapping implementations. It will also get the average difference in SNR performance among the implementations and compute the percent improvement from the Makin reference result at the 95% confidence level (p>0.05).\n",
"\n",
"Additionally, since a large increase in SNR performance was observed for this project's KF unsupervised, it will be plotted against other Makin KF variants under a specific case of 64ms binning and for each kinematic state. This will give a sense of if the increase in performance pushes this decoder to a level that is similar to that demonstrated by the KF supervised and KF unsupervised with dynamic mapping decoders."
]
},
{
"cell_type": "code",
"execution_count": 685,
"id": "de5e78d0-343a-444b-8626-69e81208a360",
"metadata": {},
"outputs": [
{
"data": {
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",
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