{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "execution": {}, "id": "view-in-github" }, "source": [ "\"Open   \"Open" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Loading of Miller ECoG data of the joystick track task\n", "\n", "includes some visualizations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "#@title Data retrieval\n", "import os, requests\n", "\n", "fname = 'joystick_track.npz'\n", "url = \"https://osf.io/6jncm/download\"\n", "\n", "if not os.path.isfile(fname):\n", " try:\n", " r = requests.get(url)\n", " except requests.ConnectionError:\n", " print(\"!!! Failed to download data !!!\")\n", " else:\n", " if r.status_code != requests.codes.ok:\n", " print(\"!!! Failed to download data !!!\")\n", " else:\n", " with open(fname, \"wb\") as fid:\n", " fid.write(r.content)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title Install packages (`nilearn`, `nimare`. `duecredit`), import `matplotlib` and set defaults\n", "# install packages to visualize brains and electrode locations\n", "!pip install nilearn --quiet\n", "!pip install nimare --quiet\n", "!pip install duecredit --quiet\n", "\n", "from matplotlib import rcParams\n", "from matplotlib import pyplot as plt\n", "\n", "rcParams['figure.figsize'] = [20, 4]\n", "rcParams['font.size'] = 15\n", "rcParams['axes.spines.top'] = False\n", "rcParams['axes.spines.right'] = False\n", "rcParams['figure.autolayout'] = True" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['V', 'targetX', 'targetY', 'cursorX', 'cursorY', 'locs', 'hemisphere', 'lobe', 'gyrus', 'Brodmann_Area'])\n" ] } ], "source": [ "# @title Data loading\n", "import numpy as np\n", "\n", "alldat = np.load(fname, allow_pickle=True)['dat']\n", "\n", "# Select just one of the recordings here. This is subject 1, block 1.\n", "dat = alldat[0][0]\n", "\n", "print(dat.keys())" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "# Dataset info #\n", "\n", "This is one of multiple ECoG datasets from Miller 2019, recorded in clinical settings with a variety of tasks. Raw data here:\n", "\n", "https://exhibits.stanford.edu/data/catalog/zk881ps0522\n", "\n", "`dat` contain 4 sessions from 4 subjects, and was used in these papers: \n", "\n", "- Schalk, G., et al. \"Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.\" Journal of Neural Engineering 4.3 (2007): 264. doi: [10.1088/1741-2560/4/3/012](https://doi.org/10.1088/1741-2560/4/3/012)\n", "\n", "- Schalk, Gerwin, et al. \"Two-dimensional movement control using electrocorticographic signals in humans.\" Journal of Neural Engineering 5.1 (2008): 75. doi: [10.1088/1741-2560/5/1/008](https://doi.org/10.1088/1741-2560/5/1/008)\n", "\n", "
\n", "\n", "From the dataset readme: \n", "\n", "*During the study, each patient was in a semi-recumbent position in a hospital bed about 1 m from a computer monitor. The patient used a joystick to maneuver a white cursor track a green target moving counter-clockwise in a circle of diameter 85% of monitor height ~1m away. The hand used to control the joystick was contralateral to the implanted electrode array.*\n", "\n", "
\n", "\n", "We also know that subject 0 was implanted in the left temporal lobe, while subject 2 was implanted in the right frontal lobe. \n", "\n", "Sample rate is always 1000Hz, and the ECoG data has been notch-filtered at 60, 120, 180, 240 and 250Hz, followed by z-scoring across the entire recording and conversion to float16 to minimize size. \n", "\n", "Variables are: \n", "* `dat['V']`: continuous voltage data (time by channels)\n", "* `dat['targetX']`: position of the target on the screen\n", "* `dat['targetY']`: position of the target on the screen\n", "* `dat['cursorX']`: X position of the cursor controlled by the joystick \n", "* `dat['cursorY']`: X position of the cursor controlled by the joystick \n", "* `dat['locs`]: three-dimensional coordinates of the electrodes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from nilearn import plotting\n", "from nimare import utils\n", "\n", "plt.figure(figsize=(8, 8))\n", "locs = dat['locs']\n", "view = plotting.view_markers(utils.tal2mni(locs),\n", " marker_labels=['%d'%k for k in np.arange(locs.shape[0])],\n", " marker_color='purple',\n", " marker_size=5)\n", "view" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# compute correlations between voltage data and X/Y position of cursor\n", "from scipy import signal\n", "dat = alldat[0][3]\n", "\n", "V = dat['V'].astype('float32')\n", "\n", "nt, nchan = V.shape\n", "\n", "targetX = dat['targetX'].flatten()\n", "targetY = dat['targetY'].flatten()\n", "\n", "cx = np.zeros(nchan, )\n", "cy = np.zeros(nchan, )\n", "for j in range(nchan):\n", " cx[j] = np.corrcoef(V[:, j], targetX)[0, 1]\n", " cy[j] = np.corrcoef(V[:, j], targetY)[0, 1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplot(1, 4, 1)\n", "plt.plot(cx)\n", "plt.plot(cy)\n", "plt.ylabel('correlation with\\n X / Y position of cursor')\n", "plt.xlabel('channel index')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# this one needs a lot more plots!\n", "# for some reason, I only see meaningful correlations in subjects 2 and 3,\n", "# but it's possible that there is spectral information that is more useful in those subjects" ] } ], "metadata": { "colab": { "collapsed_sections": [], "include_colab_link": true, "name": "load_ECoG_joystick_track", "provenance": [], "toc_visible": true }, "kernel": { "display_name": "Python 3", "language": "python", "name": "python3" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }