_00_generate_band_power_topomap_image_from_timeseries_signal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f1bf0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mne\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49c99de",
   "metadata": {},
   "source": [
    "### STEP 1: Load and preprocess EEG signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6687eb06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from /mnt/ssd_4tb_0/shared/TUH_EEG_ABNORMAL/v2.0.0/edf/train/normal/01_tcp_ar/079/00007988/s002_2012_02_20/00007988_s002_t000.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 349249  =      0.000 ...  1396.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 45 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 45.00 Hz\n",
      "- Upper transition bandwidth: 11.25 Hz (-6 dB cutoff frequency: 50.62 Hz)\n",
      "- Filter length: 825 samples (3.300 sec)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "        \n",
       "        <td>February 20, 2012  10:15:46 GMT</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        \n",
       "        <td>Not available</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>19 EEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>250.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>1.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>45.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Filenames</th>\n",
       "        <td>00007988_s002_t000.edf</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Duration</th>\n",
       "        <td>00:23:17 (HH:MM:SS)</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<RawEDF | 00007988_s002_t000.edf, 19 x 349250 (1397.0 s), ~50.6 MB, data loaded>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example file\n",
    "\n",
    "edf_f = '/mnt/ssd_4tb_0/shared/TUH_EEG_ABNORMAL/v2.0.0/edf/train/normal/01_tcp_ar/079/00007988/s002_2012_02_20/00007988_s002_t000.edf'\n",
    "raw = mne.io.read_raw_edf(edf_f, preload=True)\n",
    "\n",
    "raw = raw.filter(l_freq=1.0, h_freq=45.0)\n",
    "\n",
    "# standardize channel names\n",
    "new_names = dict((ch_name, re.sub(r\"-[a-zA-Z]+\", \"\", ch_name).lower().replace('t3', 't7').replace('t4', 't8')\n",
    "                .replace('t5', 'p7').replace('t6', 'p8').replace('eeg ', '').replace(' ', ''))\n",
    "    for ch_name in raw.ch_names)\n",
    "raw.rename_channels(new_names)\n",
    "\n",
    "# keep only eeg channels\n",
    "CH_NAMES_10_20 = [\n",
    "'fp1', 'f3', 'f7', 'c3', 't7', 'p3', 'p7', 'o1', 'fp2', 'f4', 'f8',\n",
    "       'c4', 't8', 'p4', 'p8', 'o2', 'fz', 'cz', 'pz'\n",
    "]\n",
    "raw = raw.pick_channels(CH_NAMES_10_20, ordered=True)\n",
    "raw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56baa0f",
   "metadata": {},
   "source": [
    "### STEP 2: Create EEG epochs (timeseries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "880474fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "139 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 139 events and 2500 original time points ...\n",
      "0 bad epochs dropped\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(20, 19, 2500)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs = mne.make_fixed_length_epochs(raw, duration=10, preload=True)\n",
    "\n",
    "# for illustration only\n",
    "epochs = epochs[:20]\n",
    "\n",
    "montage = mne.channels.make_standard_montage('standard_1020')\n",
    "epochs.set_montage(montage, match_case=False, match_alias=True, on_missing='raise')\n",
    "\n",
    "mne.set_eeg_reference(epochs, ref_channels='average')\n",
    "\n",
    "epochs.get_data().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7672c03",
   "metadata": {},
   "source": [
    "### STEP 3: EEG epochs (timeseries) --> EEG epochs (topomap image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2883b02a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective window size : 1.024 (s)\n",
      "(20, 5, 19)\n"
     ]
    }
   ],
   "source": [
    "from utils.features import eeg_power_in_bands\n",
    "\n",
    "_, epochs_bandpower_data = eeg_power_in_bands(epochs)\n",
    "epochs_bandpower_data = np.swapaxes(epochs_bandpower_data, 0, 1)\n",
    "print(epochs_bandpower_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed90649f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch_idx = 10\n",
    "band_idx = 3\n",
    "\n",
    "im, _ = mne.viz.plot_topomap(epochs_bandpower_data[epoch_idx, band_idx, :], epochs.info, \n",
    "                             sensors=False, contours=False, cmap='gray', show=True, \n",
    "                             outlines='head', res=32, extrapolate='head'\n",
    "                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32728442",
   "metadata": {},
   "source": [
    "### STEP 4: EEG epochs (topomap image) --> EEG epochs (topomap image) numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c5d7fd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.topomaps import _img_to_array, _remove_head_cartoon\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "num_epochs = len(epochs_bandpower_data)\n",
    "epochs_topomap_images = np.zeros((num_epochs, 5, 100, 100))\n",
    "\n",
    "for epoch_idx in range(num_epochs):\n",
    "    for band_idx in range(5):\n",
    "\n",
    "        im, _ = mne.viz.plot_topomap(epochs_bandpower_data[epoch_idx, band_idx, :], epochs.info, \n",
    "                                     sensors=False, contours=False, cmap='gray', show=True, \n",
    "                                     outlines='head', res=32, extrapolate='head')\n",
    "\n",
    "        fig_arr = _img_to_array(im)\n",
    "    #     print(fig_arr.shape)\n",
    "\n",
    "        clean_fig_arr = _remove_head_cartoon(fig_arr)\n",
    "    #     print(clean_fig_arr.shape)\n",
    "\n",
    "        grayscale = clean_fig_arr[:,:,0] / 255.0\n",
    "    #     print(grayscale.shape)\n",
    "\n",
    "        epochs_topomap_images[epoch_idx, band_idx, :, :] = grayscale\n",
    "        \n",
    "    plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d51dd1c5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# same as previous plot above\n",
    "epoch_idx = 10\n",
    "band_idx = 3\n",
    "\n",
    "plt.imshow(epochs_topomap_images[epoch_idx, band_idx, :, :], interpolation='nearest', cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43c33da",
   "metadata": {},
   "source": [
    "### STEP 5: (...... use topomap images in PyTorch dataloader for model training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db70ef06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}