{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import pandas as pd\n", "import seaborn as sns\n", "from scipy.interpolate import interp1d\n", "from scipy.ndimage import gaussian_filter1d\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.preprocessing import StandardScaler\n", "import matplotlib.pyplot as plt\n", "from systole.utils import time_shift\n", "from systole.detection import oxi_peaks, rr_artefacts\n", "from systole.correction import correct_peaks\n", "from systole.utils import heart_rate, to_epochs\n", "from mne.stats import permutation_cluster_test, permutation_cluster_1samp_test" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "parent = os.path.join(os.getcwd(), os.pardir)\n", "path = os.path.join(os.path.abspath(parent), 'Data', 'Raw')\n", "subjects = [f for f in os.listdir(path) if len(f) == 5]\n", "filename = path + '/{nSub}/{nSub}_Aro_{arousal}_Val_{valence}_Learn_{learningTime}_Block_{block}.txt' # Template for signal files" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# For bad signal\n", "reject = ['11141', '11137']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "subjects = [f for f in subjects if f not in reject]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def extract_epochs(signal, triggers, valence, arousal, learningTime, nSub, tmin, tmax,\n", " draw_figure=False, figure_name=None):\n", " \"\"\"This function will find peaks in signal, reject artefacts and epoch the raw data.\n", " If draw_figure is True, will also create a report figure.\"\"\"\n", "\n", " # Find peaks\n", " signal, peaks = oxi_peaks(signal, noise_removal=False)\n", " \n", " # Correct extra and missed peaks\n", " peaks_correction = correct_peaks(peaks)\n", " peaks = peaks_correction['clean_peaks']\n", "\n", " # Extract instantaneous heartrate\n", " hr, time = heart_rate(peaks, sfreq=1000, unit='bpm', kind='previous')\n", " hr = gaussian_filter1d(hr, sigma=100)\n", "\n", " # Interpolate HR to 75 Hz\n", " f = interp1d(time, hr, fill_value=\"extrapolate\")\n", " new_time = np.arange(0, time[-1], 1/75)\n", " hr = f(new_time)\n", "\n", " # Outliers detection\n", " artefacts = rr_artefacts(np.diff(np.where(peaks)[0]))\n", " outliers = artefacts['ectopic'] | artefacts['short'] | artefacts['long'] | artefacts['extra'] | artefacts['missed']\n", " \n", " # Create bads vector\n", " reject = np.zeros(len(peaks))\n", " for i in np.where(outliers)[0]:\n", " reject[np.where(peaks)[0][i-1]:np.where(peaks)[0][i+1]] = 1\n", " \n", " # Interpolate HR to 75 Hz\n", " f = interp1d(time, reject, fill_value=\"extrapolate\")\n", " new_time = np.arange(0, time[-1], 1/75)\n", " reject = f(new_time)\n", "\n", " # Epoching\n", " epoch = to_epochs(hr, triggers, sfreq=75, event_val=2, tmin=tmin, tmax=tmax, apply_baseline=(-1, 0), reject=reject)\n", " \n", " if draw_figure:\n", " plot_epochs(epoch=epoch, triggers=triggers, peaks=peaks, time=time, new_time=new_time, hr=hr,\n", " outliers=outliers, figure_name=figure_name)\n", "\n", " return epoch" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "tmin, tmax = -1, 6\n", "final_df = pd.DataFrame([])\n", "for nSub in subjects:\n", " \n", " # Import behavioural data\n", " df = pd.read_csv(os.path.join(path, nSub, 'Subject_{}.txt'.format(nSub)), na_values=['Nan'])\n", "\n", " # Loop through conditions\n", " for val in ['High', 'Low']:\n", " \n", " for aro in ['High', 'Low']:\n", " \n", " X, y = None, np.array([])\n", "\n", " for learningTime in [30, 60, 90]:\n", " \n", " # Filter for condition\n", " this_df = df.copy()[(df.Arousal == aro) & (df.Valence == val) & (df.LearningTime == learningTime)]\n", " nBlock = this_df.nBlock.iloc[0] # Get block numbers \n", "\n", " # Remove NaN, fast and slow RT\n", " drop = ((this_df.RT.isnull() | this_df.Confidence.isnull()) | \n", " (this_df.RT < .1) | (this_df.RT > (this_df.RT.median() + (3 * this_df.RT.std())))) \n", " \n", " # Import pulse oximeter recording\n", " signal_file = filename.format(nSub=nSub, arousal=aro, valence=val, learningTime=learningTime, block=int(nBlock))\n", " signal_df = pd.read_csv(signal_file)\n", "\n", " # Extract epoch and plot artefact rejection\n", " epoch = extract_epochs(signal_df.signal.to_numpy(),\n", " signal_df.triggers.to_numpy(),\n", " valence=val, arousal=aro, learningTime=learningTime, nSub=nSub,\n", " tmin=tmin, tmax=tmax)\n", " \n", " # Drop no response and artefact trials\n", " drop = drop | np.isnan(epoch.mean(axis=1))\n", " epoch = epoch[~drop, :]\n", "\n", " # Store data for linear regression\n", " y = np.append(y, this_df.Confidence.to_numpy()[~drop])\n", " if X is not None:\n", " X = np.concatenate([X, epoch])\n", " else:\n", " X = epoch\n", "\n", " # Fit linear regression to predict Confidence\n", " std_clf = make_pipeline(StandardScaler(), LinearRegression())\n", "\n", " beta = []\n", " for i in range(X.shape[1]):\n", " reg = std_clf.fit(y.reshape(-1, 1), X[:, i].reshape(-1, 1))\n", " beta.append(reg.named_steps['linearregression'].coef_[0][0])\n", "\n", " final_df = final_df.append(pd.DataFrame({'Valence': val,\n", " 'Arousal': aro,\n", " 'LearningTime': learningTime,\n", " 'Time': np.arange(tmin, tmax, 1/75),\n", " 'Subject': nSub,\n", " 'Beta': np.asarray(beta)}))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "final_df.to_csv(os.path.join(os.path.abspath(parent), 'Data', 'Preprocessed', 'regression.txt'))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "final_df = pd.read_csv(os.path.join(os.path.abspath(parent), 'Data', 'Preprocessed', 'regression.txt'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Valence" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stat_fun(H1): min=0.000046 max=7.876467\n", "Running initial clustering\n", "Found 1 clusters\n", "Permuting 4999 times...\n", "[............................................................] 100.00% |\n", "Computing cluster p-values\n", "Done.\n", "stat_fun(H1): min=-1.375083 max=7.712130\n", "Running initial clustering\n", "Found 1 clusters\n", "Permuting 4999 times...\n", "[............................................................] 100.00% |\n", "Computing cluster p-values\n", "Done.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.set_context('talk')\n", "plt.figure(figsize=(8, 6))\n", "\n", "df = final_df.copy().groupby(['Subject', 'Valence', 'Time'], as_index=False).mean()\n", "df.Valence = df.Valence.replace(['Low', 'High'], ['Negative', 'Positive'])\n", "\n", "data = []\n", "for val in ['Negative', 'Positive']:\n", " data.append(df[(df['Valence']==val)].pivot(index='Time',columns='Subject')['Beta'].to_numpy())\n", "data = np.asarray(data)\n", "\n", "####################\n", "# Test for condition\n", "####################\n", "T_obs, clusters, cluster_p_values, H0 = \\\n", " permutation_cluster_test([data[0].T, data[1].T],\n", " n_permutations=5000, threshold=6)\n", "\n", "times = np.arange(-1, 6, 1/75)\n", "for i, c in enumerate(clusters):\n", " if cluster_p_values[i] <= 0.05:\n", " c = c[0]\n", " plt.axvspan(times[c.start], times[c.stop - 1],\n", " color='r', alpha=0.3)\n", "\n", "######################\n", "# Test for null effect\n", "######################\n", "T_obs, clusters, cluster_p_values, H0 = \\\n", " permutation_cluster_1samp_test(data.mean(0).T, n_permutations=5000,\n", " threshold=6, tail=0)\n", "\n", "times = np.arange(-1, 6, 1/75)\n", "for i, c in enumerate(clusters):\n", " if cluster_p_values[i] <= 0.05:\n", " c = c[0]\n", " plt.axvspan(times[c.start], times[c.stop - 1],\n", " color='gray', alpha=0.3)\n", "\n", "sns.lineplot(data=df, x='Time', y='Beta', hue='Valence', ci=68, n_boot=10000, palette=['firebrick', 'steelblue'])\n", "\n", "plt.axhline(y=0, linestyle='--', color='gray')\n", "plt.axvline(x=0, linestyle='--', color='k')\n", "plt.ylabel(r'Mean $\\beta$')\n", "plt.xlabel('Time (s)')\n", "plt.axhline(y=0, linestyle='--', color='gray')\n", "plt.axvline(x=0, linestyle='--', color='k')\n", "plt.xlim(-1, 6)\n", "plt.ylim(-.4, 1)\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", "dirName = os.path.join(os.path.abspath(parent), 'Figures')\n", "plt.savefig(dirName + 'Valence - Beta.svg', dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Arousal" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stat_fun(H1): min=0.000000 max=1.898343\n", "Running initial clustering\n", "Found 0 clusters\n", "stat_fun(H1): min=-1.375083 max=7.712130\n", "Running initial clustering\n", "Found 1 clusters\n", "Permuting 4999 times...\n", "[ ] 1.06% |" ] }, { "name": "stderr", "output_type": "stream", "text": [ "<ipython-input-48-49940b5e9cbf>:16: RuntimeWarning: No clusters found, returning empty H0, clusters, and cluster_pv\n", " n_permutations=5000, threshold=6)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[............................................................] 100.00% |\n", "Computing cluster p-values\n", "Done.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.set_context('talk')\n", "plt.figure(figsize=(8, 6))\n", "\n", "df = final_df.copy().groupby(['Subject', 'Arousal', 'Time'], as_index=False).mean()\n", "\n", "data = []\n", "for val in ['High', 'Low']:\n", " data.append(df[(df['Arousal']==val)].pivot(index='Time',columns='Subject')['Beta'].to_numpy())\n", "data = np.asarray(data)\n", "\n", "####################\n", "# Test for condition\n", "####################\n", "T_obs, clusters, cluster_p_values, H0 = \\\n", " permutation_cluster_test([data[0].T, data[1].T],\n", " n_permutations=5000, threshold=6)\n", "\n", "times = np.arange(-1, 6, 1/75)\n", "for i, c in enumerate(clusters):\n", " if cluster_p_values[i] <= 0.05:\n", " c = c[0]\n", " plt.axvspan(times[c.start], times[c.stop - 1],\n", " color='r', alpha=0.3)\n", "\n", "######################\n", "# Test for null effect\n", "######################\n", "T_obs, clusters, cluster_p_values, H0 = \\\n", " permutation_cluster_1samp_test(data.mean(0).T, n_permutations=5000,\n", " threshold=6, tail=0)\n", "\n", "times = np.arange(-1, 6, 1/75)\n", "for i, c in enumerate(clusters):\n", " if cluster_p_values[i] <= 0.05:\n", " c = c[0]\n", " plt.axvspan(times[c.start], times[c.stop - 1],\n", " color='gray', alpha=0.3)\n", " \n", "sns.lineplot(data=df, x='Time', y='Beta', hue='Arousal', style='Arousal', ci=68, n_boot=10000, palette=['gray', 'gray'])\n", "\n", "plt.axhline(y=0, linestyle='--', color='gray')\n", "plt.axvline(x=0, linestyle='--', color='k')\n", "plt.ylabel(r'Mean $\\beta$')\n", "plt.xlabel('Time (s)')\n", "plt.axhline(y=0, linestyle='--', color='gray')\n", "plt.axvline(x=0, linestyle='--', color='k')\n", "plt.xlim(-1, 6)\n", "plt.ylim(-.4, 1)\n", "\n", "sns.despine()\n", "plt.tight_layout()\n", "\n", "dirName = os.path.join(os.path.abspath(parent), 'Figures')\n", "plt.savefig(dirName + 'Arousal - Beta.svg', dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Extract trials timing" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "responseTime_df = pd.DataFrame([])\n", "for nSub in subjects:\n", " \n", " # Import behavioural data\n", " df = pd.read_csv(os.path.join(path, nSub, 'Subject_{}.txt'.format(nSub)), na_values=['Nan'])\n", "\n", " # Loop through conditions\n", " for val in ['High', 'Low']:\n", " \n", " for aro in ['High', 'Low']:\n", " \n", " X, y = None, np.array([])\n", "\n", " for learningTime in [30, 60, 90]:\n", " \n", " # Filter for condition\n", " this_df = df.copy()[(df.Arousal == aro) & (df.Valence == val) & (df.LearningTime == learningTime)]\n", " nBlock = this_df.nBlock.iloc[0] # Get block numbers \n", " \n", " # Import pulse oximeter recording\n", " signal_file = filename.format(nSub=nSub, arousal=aro, valence=val, learningTime=learningTime, block=int(nBlock))\n", " signal_df = pd.read_csv(signal_file)\n", "\n", " start = np.where(signal_df.triggers.to_numpy() == 2)[0]\n", " decision = np.where(signal_df.triggers.to_numpy() == 3)[0]\n", " metacog = np.where(signal_df.triggers.to_numpy() == 4)[0]\n", " metacog = metacog[metacog > start[0]] # Remove triggers from learning\n", " \n", " decisionTR = np.median(time_shift(start, decision))/75\n", " metacogTR = np.median(time_shift(start, metacog))/75\n", " \n", " responseTime_df = responseTime_df.append(pd.DataFrame({'Valence': val,\n", " 'Arousal': aro,\n", " 'LearningTime': learningTime,\n", " 'decisionTR': [decisionTR],\n", " 'metacogTR': [metacogTR],\n", " 'Subject': nSub}), ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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tH75dmdYKiurMn/cpQ9JCSft8TkREL7AemAvMGVbOcuCXEbGusjMwazyu8ZpNfx8Hvg/8p6SvAY+Tfal+M/BBsmu0V49RxmpgO1lz8n0l688FLpV0N1nt+nWyW4/eB9wVEUPN2PkAGu8Bbp38KZnVLydes2kuIp6X9A7gCrJE+4dk9/o+D9wLjHkbUES8KukO4CP5tds9+aZO4O1kPacXk113Xg9cDgy/vnsmWS141WTPyayeechIMwOykbTI7r29OCK+MoHjfwJsjIgzqhyaWV1x4jWzIZKuB84GlpbUeis57nSymvWyiHi6VvGZ1QMnXjMzs4Tcq9nMzCwhJ14zM7OEnHjNzMwScuI1MzNLyInXzMwsISdeMzOzhJx4zczMEnLiNTMzS+j/AbUxY/G53obaAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 576x72 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df = responseTime_df.copy().groupby(['Subject'], as_index=False).mean()\n", "sns.set_context('talk')\n", "fig, ax = plt.subplots(figsize=(8, 1))\n", "sns.boxplot(data=df, x='decisionTR', orient='h', palette=['#50b689'], width=.5)\n", "sns.boxplot(data=df, x='metacogTR', orient='h', palette=['#04702f'], width=.5)\n", "plt.axvline(x=0, linestyle='--', color='k')\n", "plt.xlim(-1, 6)\n", "plt.xlabel('Time (s)')\n", "\n", "ax.set_yticks([])\n", "\n", "sns.despine()\n", "\n", "dirName = os.path.join(os.path.abspath(parent), 'Figures')\n", "plt.savefig(dirName + 'RT - Beta.svg', dpi=300)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }