3.eeg.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "# Copyright 2023 by the author(s) of CHI2023 Late Breaking Work\n",
    "# \"Exploring Physiological Correlates of Visual Complexity Adaptation:\n",
    "# Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR\n",
    "# Adaptive Systems\". All rights reserved.\n",
    "#\n",
    "# Use of this source code is governed by a GPLv3 license that\n",
    "# can be found in the LICENSE file.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/changkun/miniconda3/envs/chi23lbw/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import neurokit2 as nk\n",
    "import mne\n",
    "from mne_icalabel import label_components\n",
    "mne.set_log_level('WARNING')\n",
    "from scipy.integrate import simpson # composite Simpson's rule\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['text.latex.preamble'] = r'''\\usepackage{libertine}\\usepackage{fontawesome5}\\usepackage[dvipsnames]{xcolor}'''\n",
    "plt.rcParams['ps.usedistiller'] = 'xpdf'\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='ticks')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from lbw import get_event_intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "chan_names = ['Fz', 'P3', 'Pz', 'P4', 'PO7', 'Oz', 'PO8']\n",
    "chan_maps = {\n",
    "    'Value0': 'Fz',\n",
    "    'Value1': 'P3',\n",
    "    'Value2': 'Pz',\n",
    "    'Value3': 'P4',\n",
    "    'Value4': 'PO7',\n",
    "    'Value5': 'Oz',\n",
    "    'Value6': 'PO8',\n",
    "}\n",
    "picks_alpha = ['P3', 'Pz', 'P4']\n",
    "picks_theta = ['Fz', 'Pz']\n",
    "picks_beta = ['Fz', 'Pz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_eeg(pid):\n",
    "    dfEEG = pd.read_csv(f\"./data/ID{pid}-EEG.csv\")\n",
    "    dfState = pd.read_csv(f\"./data/ID{pid}-state.csv\")\n",
    "    dfAdaptation = pd.read_csv(f\"./data/ID{pid}-adaptation.csv\")\n",
    "    dfEEG.rename(columns=chan_maps, inplace=True)\n",
    "    dfEEG.drop(\"TimeLsl\", axis =1, inplace=True)\n",
    "    dfEEG['BlockNumber'] = -1\n",
    "    intervals, workloads = get_event_intervals(dfAdaptation)\n",
    "\n",
    "    for idx, interval in enumerate(intervals):\n",
    "        dfEEG.loc[(dfEEG['Time'] >= interval[0]) & (dfEEG['Time'] <= interval[1]), 'workload'] = workloads[idx]\n",
    "        dfEEG.loc[(dfEEG['Time'] >= interval[0]) & (dfEEG['Time'] <= interval[1]), 'interval_id'] = idx\n",
    "    dfEEG = dfEEG[dfEEG['workload'].notna()]\n",
    "\n",
    "    dfEEG.loc[dfEEG[(dfEEG.Time > dfState['Time'][2]) & (dfEEG.Time < dfState['Time'][2+1])].index, 'BlockNumber']   = dfState['BlockNumber'][2]\n",
    "    dfEEG.loc[dfEEG[(dfEEG.Time > dfState['Time'][4]) & (dfEEG.Time < dfState['Time'][4+1])].index, 'BlockNumber']   = dfState['BlockNumber'][4]\n",
    "    dfEEG.loc[dfEEG[(dfEEG.Time > dfState['Time'][6]) & (dfEEG.Time < dfState['Time'][6+1])].index, 'BlockNumber']   = dfState['BlockNumber'][6]\n",
    "    dfEEG.loc[dfEEG[(dfEEG.Time > dfState['Time'][8]) & (dfEEG.Time < dfState['Time'][8+1])].index, 'BlockNumber']  = dfState['BlockNumber'][8]\n",
    "    dfEEG.loc[dfEEG[(dfEEG.Time > dfState['Time'][10]) & (dfEEG.Time < dfState['Time'][10+1])].index, 'BlockNumber'] = dfState['BlockNumber'][10]\n",
    "    dfEEG.loc[dfEEG[(dfEEG.Time > dfState['Time'][12]) & (dfEEG.Time < dfState['Time'][12+1])].index, 'BlockNumber'] = dfState['BlockNumber'][12]\n",
    "\n",
    "    dfAll = dfEEG.copy()\n",
    "    dfAll = dfAll.drop(dfAll[dfAll.BlockNumber < 0].index)\n",
    "    dfAll = dfAll.dropna()\n",
    "    dfAll = dfAll.sort_values(by=\"Time\")\n",
    "    return dfAll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_eeg_feature(group, freqs_range):\n",
    "    picks = mne.pick_types(group.info, meg=False, eeg=True, eog=False, stim=False)\n",
    "    psds, freqs = mne.time_frequency.psd_welch(group, proj=False, picks=picks, n_jobs=2, n_per_seg=50, n_overlap=5, n_fft=300)\n",
    "    idx = np.logical_and(freqs >= freqs_range[0], freqs <= freqs_range[1])\n",
    "    psds_mean = psds.mean(0)\n",
    "    freq_res = freqs[1] - freqs[0]\n",
    "    return simpson(np.log(psds_mean[idx]), dx=freq_res)\n",
    "\n",
    "def eeg_analysis(df_block, pid):\n",
    "    info = mne.create_info(ch_names=chan_names, sfreq=250, ch_types='eeg')\n",
    "    info.set_montage('standard_1020')\n",
    "    raw = mne.io.RawArray(df_block[chan_names].values.T, info)\n",
    "    raw_filter = raw.filter(l_freq=1, h_freq=70, fir_design='firwin2')\n",
    "    raw_filter_notch = raw_filter.notch_filter([50, 100], picks=chan_names, fir_design='firwin')\n",
    "    raw_filter_notch_ref = raw_filter_notch.set_eeg_reference('average', projection=True)\n",
    "\n",
    "    ica = mne.preprocessing.ICA(n_components=len(chan_names), random_state=42, max_iter='auto')\n",
    "    ica.fit(raw_filter_notch_ref)\n",
    "\n",
    "    ic_labels = label_components(raw_filter_notch_ref, ica, method=\"iclabel\")\n",
    "\n",
    "    labels = ic_labels[\"labels\"]\n",
    "    exclude_idx = [idx for idx, label in enumerate(labels) if label not in [\"brain\", \"other\"]]\n",
    "\n",
    "    reconst_raw = raw_filter_notch_ref.copy()\n",
    "    ica.apply(reconst_raw, exclude=exclude_idx)\n",
    "    raw_filter_notch_ref_ica = reconst_raw.copy()\n",
    "\n",
    "    alpha = compute_eeg_feature(raw_filter_notch_ref_ica.copy().pick_channels(picks_alpha), (8, 12))\n",
    "    theta = compute_eeg_feature(raw_filter_notch_ref_ica.copy().pick_channels(picks_theta), (4, 8))\n",
    "    beta  = compute_eeg_feature(raw_filter_notch_ref_ica.copy().pick_channels(picks_beta), (12, 25))\n",
    "    return pid, len(exclude_idx), alpha, theta, beta, raw_filter_notch_ref_ica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 11/11 [00:10<00:00,  1.06it/s]\n",
      "100%|██████████| 11/11 [00:58<00:00,  5.32s/it]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>workload</th>\n",
       "      <th>n_ica</th>\n",
       "      <th>alpha</th>\n",
       "      <th>theta</th>\n",
       "      <th>beta</th>\n",
       "      <th>interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1.285121</td>\n",
       "      <td>3.164314</td>\n",
       "      <td>-1.439646</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2.075275</td>\n",
       "      <td>3.703505</td>\n",
       "      <td>5.764936</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
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       "      <td>8.538782</td>\n",
       "      <td>4.835964</td>\n",
       "      <td>5.480579</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>high</td>\n",
       "      <td>0</td>\n",
       "      <td>1.940613</td>\n",
       "      <td>2.877174</td>\n",
       "      <td>7.191034</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1.904074</td>\n",
       "      <td>4.529959</td>\n",
       "      <td>1.915914</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>102</th>\n",
       "      <td>13</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>22.008694</td>\n",
       "      <td>18.039341</td>\n",
       "      <td>39.501176</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>103</th>\n",
       "      <td>14</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>10.914421</td>\n",
       "      <td>17.394413</td>\n",
       "      <td>36.898880</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>14</td>\n",
       "      <td>high</td>\n",
       "      <td>0</td>\n",
       "      <td>7.804129</td>\n",
       "      <td>13.631666</td>\n",
       "      <td>28.184074</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>14</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>9.129773</td>\n",
       "      <td>10.921125</td>\n",
       "      <td>20.889585</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>14</td>\n",
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       "      <td>13.355976</td>\n",
       "      <td>14.539402</td>\n",
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       "      <td>3.0</td>\n",
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       "<p>107 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "    pid workload n_ica      alpha      theta       beta  interval\n",
       "0     1      low     0   1.285121   3.164314  -1.439646       0.0\n",
       "1     1     high     0   2.075275   3.703505   5.764936       1.0\n",
       "2     1      low     0   8.538782   4.835964   5.480579       2.0\n",
       "3     1     high     0   1.940613   2.877174   7.191034       3.0\n",
       "4     1      low     0   1.904074   4.529959   1.915914       4.0\n",
       "..   ..      ...   ...        ...        ...        ...       ...\n",
       "102  13      low     0  22.008694  18.039341  39.501176      14.0\n",
       "103  14      low     0  10.914421  17.394413  36.898880       0.0\n",
       "104  14     high     0   7.804129  13.631666  28.184074       1.0\n",
       "105  14      low     0   9.129773  10.921125  20.889585       2.0\n",
       "106  14     high     0  13.355976  14.539402  33.669218       3.0\n",
       "\n",
       "[107 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pid_valid = [1, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14]\n",
    "df_all = {}\n",
    "\n",
    "for pid in tqdm.tqdm(pid_valid):\n",
    "    df = load_eeg(pid)\n",
    "    df_all[pid] = df\n",
    "\n",
    "df = pd.DataFrame(columns=['pid', 'workload', 'n_ica', 'alpha', 'theta', 'beta'])\n",
    "for pid in tqdm.tqdm(pid_valid):\n",
    "    df_current = df_all[pid]\n",
    "    for interval_id in df_current.interval_id.unique():\n",
    "        df_event = df_all[pid][df_all[pid].interval_id == interval_id]\n",
    "        if len(df_event) < 1000: # the data is too short to compute ica\n",
    "            continue\n",
    "\n",
    "        pid, n_ica, alpha, theta, beta, _ = eeg_analysis(df_event, pid)\n",
    "        workload = df_event['workload'].values[0]\n",
    "        df = df.append({\n",
    "            'pid': pid,\n",
    "            'n_ica': n_ica,\n",
    "            'interval': interval_id,\n",
    "            'workload': workload,\n",
    "            'alpha': alpha,\n",
    "            'theta': theta,\n",
    "            'beta': beta,\n",
    "        }, ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['alpha_theta_ratio'] = df['alpha'] / df['theta']\n",
    "df['beta_theta_ratio'] = df['beta'] / df['theta']\n",
    "df.to_csv('processed/eeg.csv')\n",
    "\n",
    "df['alpha_log'] = np.log(df['alpha'])\n",
    "df['theta_log'] = np.log(df['theta'])\n",
    "df['beta_log'] = np.log(df['beta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(6, 5))\n",
    "colors = list({'yellow' : '#FBC63D', 'blue': '#0376BB'}.values())\n",
    "sns.boxplot(x='workload', y='alpha_log', data=df, ax=axes[0], palette=colors, showfliers=False)\n",
    "sns.boxplot(x='workload', y='theta_log', data=df, ax=axes[1], palette=colors, showfliers=False)\n",
    "\n",
    "axes[0].set_ylabel('Alpha')\n",
    "axes[1].set_ylabel('Theta')\n",
    "\n",
    "axes[0].set_xticklabels(['Decrease', 'Increase'])\n",
    "axes[1].set_xticklabels(['Decrease', 'Increase'])\n",
    "\n",
    "axes[0].set_xlabel('')\n",
    "axes[1].set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/fig3a.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(6, 5))\n",
    "colors = list({'yellow' : '#FBC63D', 'blue': '#0376BB'}.values())\n",
    "sns.boxplot(x='workload', y='beta_log', data=df, ax=axes[0], palette=colors, showfliers=False)\n",
    "sns.boxplot(x='workload', y='alpha_theta_ratio', data=df, ax=axes[1], palette=colors, showfliers=False)\n",
    "\n",
    "axes[0].set_ylabel('Beta')\n",
    "axes[1].set_ylabel('Alpha Theta Ratio')\n",
    "\n",
    "axes[0].set_xticklabels(['Decrease', 'Increase'])\n",
    "axes[1].set_xticklabels(['Decrease', 'Increase'])\n",
    "\n",
    "axes[0].set_xlabel('')\n",
    "axes[1].set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/fig3b.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 11/11 [00:58<00:00,  5.34s/it]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>workload</th>\n",
       "      <th>n_ica</th>\n",
       "      <th>alpha</th>\n",
       "      <th>theta</th>\n",
       "      <th>beta</th>\n",
       "      <th>interval_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1.285121</td>\n",
       "      <td>3.164314</td>\n",
       "      <td>-1.439646</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>high</td>\n",
       "      <td>0</td>\n",
       "      <td>2.075275</td>\n",
       "      <td>3.703505</td>\n",
       "      <td>5.764936</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>8.538782</td>\n",
       "      <td>4.835964</td>\n",
       "      <td>5.480579</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>high</td>\n",
       "      <td>0</td>\n",
       "      <td>1.940613</td>\n",
       "      <td>2.877174</td>\n",
       "      <td>7.191034</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1.904074</td>\n",
       "      <td>4.529959</td>\n",
       "      <td>1.915914</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>13</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>22.008694</td>\n",
       "      <td>18.039341</td>\n",
       "      <td>39.501176</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>14</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>10.914421</td>\n",
       "      <td>17.394413</td>\n",
       "      <td>36.898880</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>14</td>\n",
       "      <td>high</td>\n",
       "      <td>0</td>\n",
       "      <td>7.804129</td>\n",
       "      <td>13.631666</td>\n",
       "      <td>28.184074</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>14</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>9.129773</td>\n",
       "      <td>10.921125</td>\n",
       "      <td>20.889585</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>14</td>\n",
       "      <td>high</td>\n",
       "      <td>0</td>\n",
       "      <td>13.355976</td>\n",
       "      <td>14.539402</td>\n",
       "      <td>33.669218</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    pid workload n_ica      alpha      theta       beta  interval_id\n",
       "0     1      low     0   1.285121   3.164314  -1.439646          0.0\n",
       "1     1     high     0   2.075275   3.703505   5.764936          1.0\n",
       "2     1      low     0   8.538782   4.835964   5.480579          2.0\n",
       "3     1     high     0   1.940613   2.877174   7.191034          3.0\n",
       "4     1      low     0   1.904074   4.529959   1.915914          4.0\n",
       "..   ..      ...   ...        ...        ...        ...          ...\n",
       "102  13      low     0  22.008694  18.039341  39.501176         14.0\n",
       "103  14      low     0  10.914421  17.394413  36.898880          0.0\n",
       "104  14     high     0   7.804129  13.631666  28.184074          1.0\n",
       "105  14      low     0   9.129773  10.921125  20.889585          2.0\n",
       "106  14     high     0  13.355976  14.539402  33.669218          3.0\n",
       "\n",
       "[107 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns=['pid', 'workload', 'n_ica', 'alpha', 'theta', 'beta'])\n",
    "\n",
    "increased_raws = []\n",
    "decreased_raws = []\n",
    "\n",
    "for pid in tqdm.tqdm(pid_valid):\n",
    "    df_current = df_all[pid]\n",
    "    for interval_id in df_current.interval_id.unique():\n",
    "        df_event = df_all[pid][df_all[pid].interval_id == interval_id]\n",
    "        if len(df_event) < 1000: # the data is too short to compute ica\n",
    "            continue\n",
    "\n",
    "        pid, n_ica, alpha, theta, beta, raw = eeg_analysis(df_event, pid)\n",
    "        workload = df_event['workload'].values[0]\n",
    "        df = df.append({'pid': pid, 'n_ica': n_ica, 'interval_id': interval_id, 'workload': workload, 'alpha': alpha, 'theta': theta, 'beta': beta}, ignore_index=True)\n",
    "\n",
    "        if workload == 'low':\n",
    "            decreased_raws.append(raw)\n",
    "        else:\n",
    "            increased_raws.append(raw)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x150 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_increase = mne.concatenate_raws(increased_raws[2:])\n",
    "raw_increase.plot_psd_topomap(ch_type='eeg', normalize=True, n_fft=300, n_overlap=5, n_per_seg=50, proj=False, show=False)\n",
    "plt.savefig('figures/fig2topomap_increase.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x150 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_decrease = mne.concatenate_raws(decreased_raws[2:])\n",
    "raw_decrease.plot_psd_topomap(ch_type='eeg', normalize=True, n_fft=300, n_overlap=5, n_per_seg=50, proj=False, show=False)\n",
    "plt.savefig('figures/fig2topomap_decrease.pdf', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chi23lbw",
   "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": "3.9.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "82ae58ca00847926547aabc807806e6cb01e8dbaa50ce6f12d50c344679ba919"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}