Langou_PSD_topomap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import mne\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "import pandas as pd\n",
    "from mne.time_frequency import tfr_morlet, psd_multitaper, psd_welch\n",
    "from copy import deepcopy\n",
    "from mne.preprocessing import create_ecg_epochs, create_eog_epochs, read_ica\n",
    "import sys\n",
    "from langouEEG import *\n",
    "\n",
    "def initData_clean(subject_name,picks_str=['O1','O2','OZ']):\n",
    "    file_path = dataRoot+'/Light/'+ subject_name + \" Data.cnt\"\n",
    "    print(dataRoot)\n",
    "    print(file_path)\n",
    "    raw = mne.io.read_raw_cnt(file_path, preload=True)\n",
    "    # picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=True, exclude='bads')\n",
    "    picks = picks_str\n",
    "    if not os.path.exists(dataRoot+('/Light')):\n",
    "        os.mkdir('Light')\n",
    "    return raw,picks,picks_str\n",
    "\n",
    "# picks_list = [['M1','M2'],['O1','O2','OZ'],['PO3','PO4','POZ'],['P1','P2','PZ'],['CP1','CP2','CPZ'],['C1','C2','CZ'],['FC1','FC2','FCZ'],['F1','F2','FZ']]\n",
    "# pos_list = ['M','O','PO','P','CP','C','FC','F']\n",
    "## For debugging \n",
    "# picks_list = [['O1','O2','OZ']]\n",
    "epochs_4F, epochs_4R, epochs_RF, epochs_RR = [], [], [], []\n",
    "picks_list = ['eeg']\n",
    "pos_list = ['O']\n",
    "# sub_list = range(13, 14)\n",
    "# [1, 4, 5, 7, 12, 14]\n",
    "sub_list = [1]\n",
    "for j in range(0, len(pos_list)):\n",
    "    init_prog()\n",
    "    for i in sub_list:\n",
    "        if i<10:\n",
    "            subject_name='S0'+str(i)\n",
    "        else:\n",
    "            subject_name='S'+str(i)\n",
    "        print(subject_name)\n",
    "        raw,picks,picks_str = initData_clean(subject_name, picks_str=picks_list[j])\n",
    "        events, event_dict=extractEvents(raw)\n",
    "        initLayout(raw)\n",
    "        epoch_RR,epoch_RF,epoch_4R,epoch_4F = extractEpochs(raw,events,picks, tmin_rest = 0,tmax_rest = 120,tmin_flick = -10,tmax_flick = 30)\n",
    "        # epochs = epoch_RR,epoch_RF,epoch_4R,epoch_4F\n",
    "        # epoch_4F.plot_psd_topomap(bands=bands)\n",
    "        # epoch_RR.plot_psd_topomap(bands=bands)\n",
    "        epochs_4F.append(epoch_4F)\n",
    "        epochs_4R.append(epoch_4R)\n",
    "        epochs_RF.append(epoch_RF)\n",
    "        epochs_RR.append(epoch_RR)\n",
    "bands = [(0, 4, 'Delta'), (4, 8, 'Theta'), (8, 12, 'Alpha'),\n",
    "         (12, 30, 'Beta'), (30, 50, 'Low Gamma'), (39, 41, 'Target')]\n",
    "# epochs_4F = mne.concatenate_epochs(epochs_4F)\n",
    "# epochs_4R = mne.concatenate_epochs(epochs_4R)\n",
    "# epochs_RF = mne.concatenate_epochs(epochs_RF)\n",
    "# epochs_RR = mne.concatenate_epochs(epochs_RR)\n",
    "\n",
    "freqs = np.logspace(*np.log10([30, 50]), num=8)\n",
    "n_cycles = freqs / 2.0\n",
    "power_4F, itc = tfr_morlet(epoch_4F, freqs=freqs, n_cycles=n_cycles, use_fft=True, return_itc=True, decim=3, n_jobs=1)\n",
    "power_4R, itc = tfr_morlet(epoch_4R, freqs=freqs, n_cycles=n_cycles, use_fft=True, return_itc=True, decim=3, n_jobs=1)\n",
    "power_RF, itc = tfr_morlet(epoch_RF, freqs=freqs, n_cycles=n_cycles, use_fft=True, return_itc=True, decim=3, n_jobs=1)\n",
    "power_RR, itc = tfr_morlet(epoch_RR, freqs=freqs, n_cycles=n_cycles, use_fft=True, return_itc=True, decim=3, n_jobs=1)\n",
    "# power_4F.plot(j, baseline=None, mode=\"logratio\")\n",
    "# epoch_4R.plot_psd_topomap(bands=bands)\n",
    "# epoch_4F.plot_psd_topomap(bands=bands)\n",
    "# epoch_RR.plot_psd_topomap(bands=bands)\n",
    "# epoch_RF.plot_psd_topomap(bands=bands)\n",
    "fig, axis = plt.subplots(1, 4, figsize=(30, 7))\n",
    "axis[0].set_ylabel(\"Power\", size='large', rotation=90)\n",
    "tminr, tmaxr=90, 120\n",
    "tminf, tmaxf = 20, 25\n",
    "fmin, fmax=39, 41\n",
    "baseline_r = (35, 36)\n",
    "baseline_f = (-10, -9)\n",
    "vmin = None\n",
    "vmax = None\n",
    "power_4R.plot_topomap(ch_type='eeg', tmin=tminr, tmax=tmaxr, fmin=fmin, fmax=fmax, baseline=baseline_r, vmin=vmin, vmax=vmax, mode='logratio', axes=axis[0], title='Rest state of 40 Hz stimualtion', show=False)\n",
    "power_4F.plot_topomap(ch_type='eeg', tmin=tminf, tmax=tmaxf, fmin=fmin, fmax=fmax, baseline=baseline_f, vmin=vmin, vmax=vmax, mode='logratio', axes=axis[1], title='Flicker state of 40 Hz stimualtion', show=False)\n",
    "power_RR.plot_topomap(ch_type='eeg', tmin=tminr, tmax=tmaxr, fmin=fmin, fmax=fmax, baseline=baseline_r, vmin=vmin, vmax=vmax, mode='logratio', axes=axis[2], title='Rest state of Random frequency stimualtion', show = False)\n",
    "power_RF.plot_topomap(ch_type='eeg', tmin=tminf, tmax=tmaxf, fmin=fmin, fmax=fmax, baseline=baseline_f, vmin=vmin, vmax=vmax, mode='logratio', axes=axis[3], title='Flicker state of Random frequency stimualtion', show=False)\n",
    "# epoch_4F.plot_psd(picks='eeg')\n",
    "# fig.savefig('Power_topomap.png')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "S01\n",
      "/data/home/viscent/Light\n",
      "/data/home/viscent/Light/Light/S01 Data.cnt\n",
      "Reading 0 ... 1249599  =      0.000 ...  2499.198 secs...\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-12-e2e10b4783ad>:17: RuntimeWarning:   Could not parse meas date from the header. Setting to None.\n",
      "  raw = mne.io.read_raw_cnt(file_path, preload=True)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "file info:\n",
      "<Info | 9 non-empty values\n",
      " bads: 1 items (Trigger)\n",
      " ch_names: FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, ...\n",
      " chs: 66 EEG\n",
      " custom_ref_applied: False\n",
      " highpass: 0.0 Hz\n",
      " lowpass: 250.0 Hz\n",
      " meas_date: unspecified\n",
      " nchan: 66\n",
      " projs: []\n",
      " sfreq: 500.0 Hz\n",
      " subject_info: 5 items (dict)\n",
      ">\n",
      "channel names:\n",
      "['FP1', 'FPZ', 'FP2', 'AF3', 'AF4', 'F7', 'F5', 'F3', 'F1', 'FZ', 'F2', 'F4', 'F6', 'F8', 'FT7', 'FC5', 'FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'FC6', 'FT8', 'T7', 'C5', 'C3', 'C1', 'CZ', 'C2', 'C4', 'C6', 'T8', 'M1', 'TP7', 'CP5', 'CP3', 'CP1', 'CPZ', 'CP2', 'CP4', 'CP6', 'TP8', 'M2', 'P7', 'P5', 'P3', 'P1', 'PZ', 'P2', 'P4', 'P6', 'P8', 'PO7', 'PO5', 'PO3', 'POZ', 'PO4', 'PO6', 'PO8', 'CB1', 'O1', 'OZ', 'O2', 'CB2', 'VEO', 'Trigger']\n",
      "time period:\n",
      "1249600\n",
      "events:\n",
      "Used Annotations descriptions: ['11', '12', '8', '9']\n",
      "{'random_flicker-60s': 1, 'random_rest-300s': 2, '40Hz_rest-300s': 3, '40Hz_flicker-60s': 4}\n",
      "1 duplicate electrode labels found:\n",
      "M1/Trigger\n",
      "Plotting 66 unique labels.\n",
      "Creating RawArray with float64 data, n_channels=66, n_times=1\n",
      "    Range : 0 ... 0 =      0.000 ...     0.000 secs\n",
      "Ready.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/data/home/viscent/Light/LangouEEG/langouEEG.py:137: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
      "  mne.viz.plot_montage(layout)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Not setting metadata\n",
      "Not setting metadata\n",
      "3 matching events found\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 3 events and 20001 original time points ...\n",
      "0 bad epochs dropped\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "3 matching events found\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 3 events and 60001 original time points ...\n",
      "0 bad epochs dropped\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "3 matching events found\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 3 events and 60001 original time points ...\n",
      "0 bad epochs dropped\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "3 matching events found\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 3 events and 20001 original time points ...\n",
      "0 bad epochs dropped\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "3 matching events found\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 3 events and 20001 original time points ...\n",
      "0 bad epochs dropped\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "power_4R.plot_topomap(ch_type='eeg', tmin=tminr, tmax=tmaxr, fmin=fmin, fmax=fmax, baseline=baseline_r, vmin=vmin, vmax=vmax, mode='logratio', title='Rest state of 40 Hz stimualtion')\n",
    "power_4F.plot_topomap(ch_type='eeg', tmin=tminf, tmax=tmaxf, fmin=fmin, fmax=fmax, baseline=baseline_f, vmin=vmin, vmax=vmax, mode='logratio', title='Flicker state of 40 Hz stimualtion')\n",
    "power_RR.plot_topomap(ch_type='eeg', tmin=tminr, tmax=tmaxr, fmin=fmin, fmax=fmax, baseline=baseline_r, vmin=vmin, vmax=vmax, mode='logratio', title='Rest state of Random frequency stimualtion')\n",
    "power_RF.plot_topomap(ch_type='eeg', tmin=tminf, tmax=tmaxf, fmin=fmin, fmax=fmax, baseline=baseline_f, vmin=vmin, vmax=vmax, mode='logratio', title='Flicker state of Random frequency stimualtion')\n",
    "\n",
    "power_4R.plot_topomap(ch_type='eeg', tmin=tminr, tmax=tmaxr, fmin=fmin, fmax=fmax, baseline=baseline_r, vmin=vmin, vmax=vmax, mode='logratio', axes=axis[0], title='Rest state of 40 Hz stimualtion')\n",
    "power_4F.plot_topomap(ch_type='eeg', tmin=tminf, tmax=tmaxf, fmin=fmin, fmax=fmax, baseline=baseline_f, vmin=vmin, vmax=vmax, mode='logratio', axes=axis[1], title='Flicker state of 40 Hz stimualtion')\n",
    "power_RR.plot_topomap(ch_type='eeg', tmin=tminr, tmax=tmaxr, fmin=fmin, fmax=fmax, baseline=baseline_r, vmin=vmin, vmax=vmax, mode='logratio', axes=axis[2], title='Rest state of Random frequency stimualtion')\n",
    "power_RF.plot_topomap(ch_type='eeg', tmin=tminf, tmax=tmaxf, fmin=fmin, fmax=fmax, baseline=baseline_f, vmin=vmin, vmax=vmax, mode='logratio', axes=axis[3], title='Flicker state of Random frequency stimualtion')"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e3970cf2a056ed71c6703e76baeca137440641ad1b48b7e7a1b0e3e35696df1b"
  },
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit ('mne': conda)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}