{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4 - Dynamic Connectivity (Group Analysis)\n", "\n", "In this short tutorial, we will build and expand on the previous tutorials by computing the dynamic connectivity, using Time-Varying Functional Connectivity Graphs.\n", "\n", "In the near future, the standard method of \"sliding window\" will be supported." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- - -" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "raw_eeg_eyes_open = np.load(\"data/eyes_opened.npy\")\n", "raw_eeg_eyes_closed = np.load(\"data/eyes_closed.npy\")\n", "\n", "num_trials, num_channels, num_samples = np.shape(raw_eeg_eyes_open)\n", "\n", "read_trials = 10" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "eeg_eyes_open = raw_eeg_eyes_open[0:read_trials, ...]\n", "eeg_eyes_closed = raw_eeg_eyes_closed[0:read_trials, ...]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dynamic connectivity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Prepare and configure the estimator object" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "from dyconnmap import tvfcg\n", "from dyconnmap.fc import IPLV" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "fb = [7.0, 13.0]\n", "cc = 4.0\n", "fs = 160.0\n", "step = 80" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "estimator = IPLV(fb, fs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Process condition \"eyes open\" " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Working ..........\n" ] } ], "source": [ "print('Working .', end='')\n", "\n", "X = np.squeeze(eeg_eyes_open[0])\n", "fcgs = tvfcg(X, estimator, fb, fs, cc, step)\n", "\n", "fcgs_eyes_open = np.array(np.real(fcgs))\n", "\n", "for i in range(1, read_trials):\n", " print('.', end='')\n", " \n", " X = np.squeeze(eeg_eyes_open[i])\n", " fcgs = tvfcg(X, estimator, fb, fs, cc, step)\n", " \n", " fcgs_eyes_open = np.vstack([fcgs_eyes_open, np.real(fcgs)])\n", " \n", "print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Process condition \"eyes closed\"" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Working ..........\n" ] } ], "source": [ "print('Working .', end='')\n", "\n", "X = np.squeeze(eeg_eyes_closed[0])\n", "fcgs = tvfcg(X, estimator, fb, fs, cc, step)\n", "\n", "fcgs_eyes_closed = np.array(np.real(fcgs))\n", "\n", "for i in range(1, read_trials):\n", " print('.', end='')\n", "\n", " X = np.squeeze(eeg_eyes_closed[i])\n", " fcgs = tvfcg(X, estimator, fb, fs, cc, step)\n", " \n", " fcgs_eyes_closed = np.vstack([fcgs_eyes_closed, np.real(fcgs)])\n", " \n", "print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FCμstates / Clustering" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from dyconnmap.cluster import NeuralGas" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "num_fcgs_eo, _, _ = np.shape(fcgs_eyes_open)\n", "num_fcgs_ec, _, _ = np.shape(fcgs_eyes_closed)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "fcgs = np.vstack([fcgs_eyes_open, fcgs_eyes_closed])\n", "num_fcgs, num_channels, num_channels = np.shape(fcgs)\n", "\n", "triu_ind = np.triu_indices_from(np.squeeze(fcgs[0, ...]), k=1)\n", "\n", "fcgs = fcgs[:, triu_ind[0], triu_ind[1]]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "rng = np.random.RandomState(0)\n", "\n", "mdl = NeuralGas(n_protos=5, rng=rng).fit(fcgs)\n", "encoding, symbols = mdl.encode(fcgs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Separate the encoded symbols based on their original groupings" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "grp_dist_eo = symbols[0:num_fcgs_eo]\n", "grp_dist_ec = symbols[num_fcgs_eo:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "h_grp_dist_eo = np.histogram(grp_dist_eo, bins=mdl.n_protos, normed=True)\n", "h_grp_dist_ec = np.histogram(grp_dist_ec, bins=mdl.n_protos, normed=True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", "\n", "ind = np.arange(mdl.n_protos)\n", "p1 = ax.bar(ind - 0.125, h_grp_dist_ec[0], 0.25, label='Eyes Closed')\n", "p2 = ax.bar(ind + 0.125, h_grp_dist_eo[0], 0.25, label='Eyes Open')\n", "\n", "ax.legend()\n", "ax.set_xlabel('Symbol Index')\n", "ax.set_ylabel('Hits %')\n", "ax.set_xticks(np.arange(mdl.n_protos))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convert state prototypes to symmetric matrices and plot them" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "protos_mtx = np.zeros((mdl.n_protos, 64, 64))\n", "\n", "for i in range(mdl.n_protos):\n", " symbol_state = np.zeros((64, 64))\n", " symbol_state[triu_ind] = mdl.protos[i, :]\n", " symbol_state = symbol_state + symbol_state.T\n", " np.fill_diagonal(symbol_state, 1.0)\n", " \n", " protos_mtx[i, :, :] = symbol_state" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mtx_min = np.min(protos_mtx)\n", "mtx_max = np.max(protos_mtx)\n", "\n", "f, ax = plt.subplots(ncols=mdl.n_protos, figsize=(12, 12))\n", "for i in range(mdl.n_protos):\n", " cax = ax[i].imshow(np.squeeze(protos_mtx[i,...]), vmin=mtx_min, vmax=mtx_max, cmap=plt.cm.Spectral)\n", " ax[i].set_title('#{0}'.format(i))\n", "\n", "# move the colorbar to the side ;)\n", "f.subplots_adjust(right=0.8)\n", "cbar_ax = f.add_axes([0.82, 0.445, 0.0125, 0.115])\n", "cb = f.colorbar(cax, cax=cbar_ax)\n", "cb.set_label('Imaginary PLV')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Separate symbols per subject\n", "\n", "Now we would like to analyze the symbols per subject, per group.\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "grp_sym_eo = np.array_split(grp_dist_eo, 10, axis=0)\n", "grp_sym_ec = np.array_split(grp_dist_ec, 10, axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Examine the first subject " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "subj1_eyes_open = grp_sym_eo[0]\n", "subj1_eyes_closed = grp_sym_ec[0]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from dyconnmap.ts import markov_matrix" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "markov_matrix_eo = markov_matrix(subj1_eyes_open)\n", "markov_matrix_ec = markov_matrix(subj1_eyes_closed)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.axes_grid1 import ImageGrid\n", "f = plt.figure(figsize=(8, 6))\n", "grid = ImageGrid(f, 111,\n", " nrows_ncols=(1,2),\n", " axes_pad=0.15,\n", " share_all=True,\n", " cbar_location=\"right\",\n", " cbar_mode=\"single\",\n", " cbar_size=\"7%\",\n", " cbar_pad=0.15,\n", " )\n", "im = grid[0].imshow(markov_matrix_eo, vmin=0.0, vmax=1.0, cmap=plt.cm.Spectral)\n", "grid[0].set_xlabel('Prototype')\n", "grid[0].set_ylabel('Prototype')\n", "grid[0].set_title('Eyes Open')\n", "\n", "im = grid[1].imshow(markov_matrix_ec, vmin=0.0, vmax=1.0, cmap=plt.cm.Spectral)\n", "grid[1].set_xlabel('Prototype')\n", "grid[1].set_ylabel('Prototype')\n", "grid[1].set_title('Eyes Close')\n", "\n", "cb = grid[1].cax.colorbar(im)\n", "cax = grid.cbar_axes[0]\n", "axis = cax.axis[cax.orientation]\n", "axis.label.set_text(\"Transition Probability\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from dyconnmap.ts import transition_rate, occupancy_time" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Transition rate\n", "===============\n", " Eyes open: 0.580\n", "Eyes closed: 0.765\n", "\n" ] } ], "source": [ "tr_eo = transition_rate(subj1_eyes_open)\n", "tr_ec = transition_rate(subj1_eyes_closed)\n", "\n", "print(\"\"\"\n", "Transition rate\n", "===============\n", " Eyes open: {0:.3f}\n", "Eyes closed: {1:.3f}\n", "\"\"\".format(tr_eo, tr_ec))" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Occupancy time\n", "==============\n", " State \t 0 \t 1 \t 2 \t 3 \t 4\n", " -----\n", " Eyes open \t 0.000 \t 0.336 \t 0.017 \t 0.059 \t 0.000\n", "Eyes closed \t 0.017 \t 0.092 \t 0.025 \t 0.050 \t 0.042\n", "\n" ] } ], "source": [ "occ_eo = occupancy_time(subj1_eyes_open)[0]\n", "occ_ec = occupancy_time(subj1_eyes_closed)[0]\n", "\n", "print(\"\"\"\n", "Occupancy time\n", "==============\n", " State \\t 0 \\t 1 \\t 2 \\t 3 \\t 4\n", " -----\n", " Eyes open \\t {0:.3f} \\t {1:.3f} \\t {2:.3f} \\t {3:.3f} \\t {4:.3f}\n", "Eyes closed \\t {5:.3f} \\t {6:.3f} \\t {7:.3f} \\t {8:.3f} \\t {9:.3f}\n", "\"\"\".format(*occ_eo, *occ_ec))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }