data_analysis_notebook_mne/data_load_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EEG data analysis and visualization using mne-python lib"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-24T11:31:59.613483Z",
     "start_time": "2024-07-24T11:31:58.387349Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import mne\n",
    "import matplotlib.pyplot as plt\n",
    "# picture presented by qt5 or inline\n",
    "%matplotlib inline"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data loading\n",
    "\n",
    "The dataset is from [BCI Competition IV 2a dataset](https://www.bbci.de/competition/iv/desc_2a.pdf)\n",
    "\n",
    "This data set consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of four different motor imagery tasks, namely the imagination of movement of the `left hand (class 1)`, `right hand (class 2)`, `both feet (class 3)`, and `tongue (class 4)`. Two sessions on different days were recorded for each subject. Each session is comprised of 6 runs separated by short breaks. One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-24T11:32:03.031080Z",
     "start_time": "2024-07-24T11:31:59.925280Z"
    }
   },
   "source": [
    "# the data set contains 18 .gdf files, we load A01T.gdf for example\n",
    "dataset_path = \"../../datasets/BCI_competition_IV_dataset/\"\n",
    "raw_data = mne.io.read_raw_gdf(dataset_path + \"BCICIV_2a_gdf/A01T.gdf\", eog=['EOG-left','EOG-central','EOG-right'], preload=True)\n",
    "print(\"channels names: \", raw_data.info['ch_names'])\n",
    "\n",
    "# the channels are 10-20system: \"Fz\", \"FC3\", \"FC1\", \"FCz\", \"FC2\", \"FC4\", \"C5\", \"C3\", \"C1\", \"Cz\",\n",
    "# \"C2\",\"C4\", \"C6\", \"CP3\", \"CP1\", \"CPz\", \"CP2\", \"CP4\", \"P1\", \"Pz\", \"P2\", \"POz\",\"EOG1\", \"EOG2\", \"EOG3\"\n",
    "# details: https://www.bbci.de/competition/iv/desc_2a.pdf\n",
    "# rename the channels\n",
    "mne.rename_channels(raw_data.info, {\n",
    "    'EEG-Fz': 'Fz', 'EEG-0': 'FC3', 'EEG-1': 'FC1', 'EEG-2':'FCz', 'EEG-3':'FC2',\n",
    "    'EEG-4': 'FC4', 'EEG-5': 'C5','EEG-C3': 'C3', 'EEG-6': 'C1', 'EEG-Cz': 'Cz',\n",
    "    'EEG-7': 'C2', 'EEG-C4': 'C4', 'EEG-8': 'C6', 'EEG-9': 'CP3', 'EEG-10':'CP1',\n",
    "    'EEG-11': 'CPz', 'EEG-12': 'CP2', 'EEG-13': 'CP4', 'EEG-14': 'P1', 'EEG-Pz': 'Pz',\n",
    "    'EEG-15':'P2', 'EEG-16':'POz'})\n",
    "print(\"new channels name: \", raw_data.info['ch_names'])\n",
    "\n",
    "# set montage of the channels\n",
    "easycap_montage = mne.channels.make_standard_montage('standard_1020')\n",
    "easycap_montage.plot()\n",
    "raw_data.set_montage(easycap_montage)\n",
    "raw_data.plot_sensors(show_names=True)\n",
    "\n",
    "# get raw data info\n",
    "print(\"raw data information: \", raw_data.info)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from D:\\Projects\\datasets\\BCI_competition_IV_dataset\\BCICIV_2a_gdf\\A01T.gdf...\n",
      "GDF file detected\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'D:\\\\Projects\\\\datasets\\\\BCI_competition_IV_dataset\\\\BCICIV_2a_gdf\\\\A01T.gdf'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[3], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# the data set contains 18 .gdf files, we load A01T.gdf for example\u001B[39;00m\n\u001B[0;32m      2\u001B[0m dataset_path \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m../../datasets/BCI_competition_IV_dataset/\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m----> 3\u001B[0m raw_data \u001B[38;5;241m=\u001B[39m \u001B[43mmne\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mio\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread_raw_gdf\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdataset_path\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m+\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mBCICIV_2a_gdf/A01T.gdf\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meog\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mEOG-left\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mEOG-central\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mEOG-right\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpreload\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mchannels names: \u001B[39m\u001B[38;5;124m\"\u001B[39m, raw_data\u001B[38;5;241m.\u001B[39minfo[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mch_names\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[0;32m      6\u001B[0m \u001B[38;5;66;03m# the channels are 10-20system: \"Fz\", \"FC3\", \"FC1\", \"FCz\", \"FC2\", \"FC4\", \"C5\", \"C3\", \"C1\", \"Cz\",\u001B[39;00m\n\u001B[0;32m      7\u001B[0m \u001B[38;5;66;03m# \"C2\",\"C4\", \"C6\", \"CP3\", \"CP1\", \"CPz\", \"CP2\", \"CP4\", \"P1\", \"Pz\", \"P2\", \"POz\",\"EOG1\", \"EOG2\", \"EOG3\"\u001B[39;00m\n\u001B[0;32m      8\u001B[0m \u001B[38;5;66;03m# details: https://www.bbci.de/competition/iv/desc_2a.pdf\u001B[39;00m\n\u001B[0;32m      9\u001B[0m \u001B[38;5;66;03m# rename the channels\u001B[39;00m\n",
      "File \u001B[1;32mD:\\anaconda3\\envs\\eeg_dl_py38\\lib\\site-packages\\mne\\io\\edf\\edf.py:1883\u001B[0m, in \u001B[0;36mread_raw_gdf\u001B[1;34m(input_fname, eog, misc, stim_channel, exclude, include, preload, verbose)\u001B[0m\n\u001B[0;32m   1881\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ext \u001B[38;5;241m!=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgdf\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m   1882\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mOnly GDF files are supported, got \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m-> 1883\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mRawGDF\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   1884\u001B[0m \u001B[43m    \u001B[49m\u001B[43minput_fname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minput_fname\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1885\u001B[0m \u001B[43m    \u001B[49m\u001B[43meog\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43meog\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1886\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmisc\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmisc\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1887\u001B[0m \u001B[43m    \u001B[49m\u001B[43mstim_channel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstim_channel\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1888\u001B[0m \u001B[43m    \u001B[49m\u001B[43mexclude\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mexclude\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1889\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpreload\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpreload\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1890\u001B[0m \u001B[43m    \u001B[49m\u001B[43minclude\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minclude\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1891\u001B[0m \u001B[43m    \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverbose\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1892\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m<decorator-gen-180>:12\u001B[0m, in \u001B[0;36m__init__\u001B[1;34m(self, input_fname, eog, misc, stim_channel, exclude, preload, include, verbose)\u001B[0m\n",
      "File \u001B[1;32mD:\\anaconda3\\envs\\eeg_dl_py38\\lib\\site-packages\\mne\\io\\edf\\edf.py:289\u001B[0m, in \u001B[0;36mRawGDF.__init__\u001B[1;34m(self, input_fname, eog, misc, stim_channel, exclude, preload, include, verbose)\u001B[0m\n\u001B[0;32m    287\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mExtracting EDF parameters from \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m...\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(input_fname))\n\u001B[0;32m    288\u001B[0m input_fname \u001B[38;5;241m=\u001B[39m os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mabspath(input_fname)\n\u001B[1;32m--> 289\u001B[0m info, edf_info, orig_units \u001B[38;5;241m=\u001B[39m \u001B[43m_get_info\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    290\u001B[0m \u001B[43m    \u001B[49m\u001B[43minput_fname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstim_channel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meog\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmisc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexclude\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpreload\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minclude\u001B[49m\n\u001B[0;32m    291\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    292\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCreating raw.info structure...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    294\u001B[0m \u001B[38;5;66;03m# Raw attributes\u001B[39;00m\n",
      "File \u001B[1;32mD:\\anaconda3\\envs\\eeg_dl_py38\\lib\\site-packages\\mne\\io\\edf\\edf.py:521\u001B[0m, in \u001B[0;36m_get_info\u001B[1;34m(fname, stim_channel, eog, misc, exclude, infer_types, preload, include)\u001B[0m\n\u001B[0;32m    518\u001B[0m eog \u001B[38;5;241m=\u001B[39m eog \u001B[38;5;28;01mif\u001B[39;00m eog \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m []\n\u001B[0;32m    519\u001B[0m misc \u001B[38;5;241m=\u001B[39m misc \u001B[38;5;28;01mif\u001B[39;00m misc \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m []\n\u001B[1;32m--> 521\u001B[0m edf_info, orig_units \u001B[38;5;241m=\u001B[39m \u001B[43m_read_header\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexclude\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minfer_types\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minclude\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    523\u001B[0m \u001B[38;5;66;03m# XXX: `tal_ch_names` to pass to `_check_stim_channel` should be computed\u001B[39;00m\n\u001B[0;32m    524\u001B[0m \u001B[38;5;66;03m#      from `edf_info['ch_names']` and `edf_info['tal_idx']` but 'tal_idx'\u001B[39;00m\n\u001B[0;32m    525\u001B[0m \u001B[38;5;66;03m#      contains stim channels that are not TAL.\u001B[39;00m\n\u001B[0;32m    526\u001B[0m stim_channel_idxs, _ \u001B[38;5;241m=\u001B[39m _check_stim_channel(stim_channel, edf_info[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mch_names\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n",
      "File \u001B[1;32mD:\\anaconda3\\envs\\eeg_dl_py38\\lib\\site-packages\\mne\\io\\edf\\edf.py:507\u001B[0m, in \u001B[0;36m_read_header\u001B[1;34m(fname, exclude, infer_types, include)\u001B[0m\n\u001B[0;32m    505\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m _read_edf_header(fname, exclude, infer_types, include)\n\u001B[0;32m    506\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m ext \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgdf\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m--> 507\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_read_gdf_header\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexclude\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minclude\u001B[49m\u001B[43m)\u001B[49m, \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    508\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    509\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(\n\u001B[0;32m    510\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mOnly GDF, EDF, and BDF files are supported, got \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    511\u001B[0m     )\n",
      "File \u001B[1;32mD:\\anaconda3\\envs\\eeg_dl_py38\\lib\\site-packages\\mne\\io\\edf\\edf.py:1056\u001B[0m, in \u001B[0;36m_read_gdf_header\u001B[1;34m(fname, exclude, include)\u001B[0m\n\u001B[0;32m   1054\u001B[0m edf_info \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mdict\u001B[39m()\n\u001B[0;32m   1055\u001B[0m events \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m-> 1056\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mfname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mrb\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mas\u001B[39;00m fid:\n\u001B[0;32m   1057\u001B[0m     version \u001B[38;5;241m=\u001B[39m fid\u001B[38;5;241m.\u001B[39mread(\u001B[38;5;241m8\u001B[39m)\u001B[38;5;241m.\u001B[39mdecode()\n\u001B[0;32m   1058\u001B[0m     edf_info[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtype\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m edf_info[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msubtype\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m version[:\u001B[38;5;241m3\u001B[39m]\n",
      "\u001B[1;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: 'D:\\\\Projects\\\\datasets\\\\BCI_competition_IV_dataset\\\\BCICIV_2a_gdf\\\\A01T.gdf'"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data visualization for example\n",
    "\n",
    "We extract 10-30s eeg data in channels EEG-Fz and EEG-Cz and plot it for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x233c68c4f10>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use sample frequent to get index\n",
    "sample_freq = raw_data.info['sfreq']\n",
    "start_stop_second = np.array([50, 52])\n",
    "start_sample, stop_sample = (start_stop_second * sample_freq).astype(int)\n",
    "selected_channel_names = ['Fz', 'Cz']\n",
    "selected_data = raw_data[selected_channel_names, start_sample:stop_sample]\n",
    "\n",
    "# selected_data[0] is the time sequence, [1] is the value of voltage\n",
    "x = selected_data[1]\n",
    "y = selected_data[0].T\n",
    "\n",
    "# plot the eeg data for example\n",
    "lines = plt.plot(x, y)\n",
    "plt.legend(lines, selected_channel_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get events envolved in analysis\n",
    "\n",
    "The position of an event in samples is contained in h.EVENT.POS. The corresponding type can be found in h.EVENT.TYP, and the duration of that particular event is stored in h.EVENT.DUR.EVENT.POS is the start of event, EVENT.TYP is type of event, EVENT.DUR is the duration of the event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annotation info: <Annotations | 603 segments: 1023 (15), 1072 (1), 276 (1), 277 (1), 32766 ...>\n",
      "annotation duration:  [4.00000e-03 1.18728e+02 4.00000e-03 8.10840e+01 4.00000e-03 1.66248e+02\n",
      " 4.00000e-03 7.50000e+00 1.25200e+00]\n",
      "annotation description:  {'768', '769', '770', '772', '771', '276', '1072', '277', '32766', '1023'}\n",
      "Used Annotations descriptions: ['1023', '1072', '276', '277', '32766', '768', '769', '770', '771', '772']\n",
      "events:  [[    0     0     5]\n",
      " [    0     0     3]\n",
      " [29683     0     5]\n",
      " [29683     0     4]\n",
      " [49955     0     5]]\n",
      "event dict:  {'1023': 1, '1072': 2, '276': 3, '277': 4, '32766': 5, '768': 6, '769': 7, '770': 8, '771': 9, '772': 10}\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the annotations(events) from raw data\n",
    "print(\"annotation info:\", raw_data.annotations)\n",
    "print(\"annotation duration: \", raw_data.annotations.duration[:9])\n",
    "print(\"annotation description: \", set(raw_data.annotations.description))\n",
    "\n",
    "# Here is the description of every type:\n",
    "# 'Rejected trial':1023,\n",
    "# 'Eye movements':1072,\n",
    "# 'Idling EEG (eyes open)':276,\n",
    "# 'Idling EEG (eyes closed)':277,\n",
    "# 'Start of a new run':32766,\n",
    "# 'Start of a trial':768,\n",
    "# *'Cue onset left (class 1)':769,\n",
    "# *'Cue onset right (class 2)':770,\n",
    "# *'Cue onset foot (class 3)':771,\n",
    "# *'Cue onset tongue (class 4)':772\n",
    "\n",
    "# get events from annotations\n",
    "events, events_dict = mne.events_from_annotations(raw_data)\n",
    "print(\"events: \", events[:5])\n",
    "print(\"event dict: \", events_dict)\n",
    "# event_dict is default to int from [1,n] n is the number of events\n",
    "\n",
    "# visual the event distributed\n",
    "events_fig = mne.viz.plot_events(events, sfreq=raw_data.info['sfreq'], first_samp=raw_data.first_samp, event_id=events_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## visualization of spectral density and scalp topography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective window size : 1.024 (s)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib qt5\n",
    "# psd figure\n",
    "spectrum = raw_data.compute_psd()\n",
    "spectrum.plot(average=True)\n",
    "\n",
    "# plot all channel\n",
    "spectrum.plot()\n",
    "\n",
    "# scalp topography\n",
    "spectrum.plot_topomap()\n",
    "plt.cla()"
   ]
  }
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