classifier-single-stage-two-or-three-classes.ipynb

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   "pygments_lexer": "ipython3",
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   "display_name": "Python 3.7.10 64-bit ('eeg': conda)"
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pprint\n",
    "import mne\n",
    "import pyxdf\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "from scipy import signal\n",
    "\n",
    "import util.preprocess_util as preprocess\n",
    "import util.analysis_util as analysis\n",
    "import util.visualise_util as visualise\n",
    "from util.data_util import EEG_Dataset, get_trials, get_good_trials\n",
    "from models.lda import *\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "source": [
    "# Load data from xdf file\n",
    "\n",
    "From the Unicorn Stream\n",
    "- `eeg_data`: the 8 channel raw eeg data\n",
    "- `eeg_timestamps`: timestamp for each eeg sample\n",
    "- `fs`: the sampling frequency\n",
    "\n",
    "From the Open Vibe Marker Stream\n",
    "- `event_codes`: array of codes sent to the marker stream at the start of each event \n",
    "- `event_time_stamps`: array of timestamps signifying the start of each event\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Data succesfullly loaded from data/Sandra_MI_1h.xdf\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR = Path('./data')\n",
    "DATA_FILE_NAME = 'Sandra_MI_1h.xdf'\n",
    "DATA_FILE = DATA_DIR / DATA_FILE_NAME\n",
    "\n",
    "dataset = EEG_Dataset(DATA_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "EEG data shape: (8, 1023780)\nSampling Frequency: 250\nTotal number of events: 1213\n('Label->Code: \\n'\n \"{'beep': 0, 'cue_no_blink': 1, 'imagery_no_blink': 2, 'break': 3, \"\n \"'cue_handR': 4, 'imagery_handR': 5, 'cue_handL': 6, 'imagery_handL': 7, \"\n \"'idle': 8, 'mi': 9}\")\nEvent code for left MI: 7\nEvent code for right MI: 5\nCount left MI events: 200\nCount right MI events: 200\n"
     ]
    }
   ],
   "source": [
    "# Extract the EEG data\n",
    "eeg_data = dataset.eeg_data\n",
    "eeg_timestamps = dataset.eeg_timestamps\n",
    "fs = dataset.fs\n",
    "\n",
    "print(f\"EEG data shape: {eeg_data.shape}\")\n",
    "print(f'Sampling Frequency: {fs}')\n",
    "\n",
    "# Extract the marker stream data\n",
    "event_codes = dataset.event_codes\n",
    "event_labels = dataset.event_labels\n",
    "label2code = dataset.label2code\n",
    "label2code['idle'] = 8\n",
    "label2code['mi'] = 9\n",
    "print(f'Total number of events: {event_codes.shape[0]}')\n",
    "pprint.pprint(f'Label->Code: \\n{label2code}')\n",
    "print(f'Event code for left MI: {label2code[\"imagery_handL\"]}')\n",
    "print(f'Event code for right MI: {label2code[\"imagery_handR\"]}')\n",
    "print(f'Count left MI events: {np.sum(event_codes == label2code[\"imagery_handL\"])}')\n",
    "print(f'Count right MI events: {np.sum(event_codes == label2code[\"imagery_handR\"])}')"
   ]
  },
  {
   "source": [
    "# Build an event time-series/stream\n",
    "\n",
    "Create a time series from the event codes which is the same length as the EEG data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_timestamps, event_timestamps, event_timeseries = dataset.build_event_timeseries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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330.6505 \nL 145.945829 330.6505 \nL 145.968126 330.6505 \nL 145.990423 330.6505 \nL 146.012721 330.6505 \nL 146.035018 330.6505 \nL 146.057315 330.6505 \nL 146.079612 330.6505 \nL 146.10191 330.6505 \nL 146.124207 330.6505 \nL 146.146504 330.6505 \nL 146.168801 330.6505 \nL 146.191099 330.6505 \nL 146.213396 330.6505 \nL 146.235693 330.6505 \nL 146.25799 330.6505 \nL 146.280288 330.6505 \nL 146.302585 330.6505 \nL 146.324882 330.6505 \nL 146.347179 330.6505 \nL 146.369477 330.6505 \nL 146.391774 330.6505 \nL 146.414071 330.6505 \nL 146.436369 330.6505 \nL 146.458666 330.6505 \nL 146.480963 330.6505 \nL 146.50326 330.6505 \nL 146.525558 330.6505 \nL 146.547855 330.6505 \nL 146.570152 330.6505 \nL 146.592449 330.6505 \nL 146.614747 330.6505 \nL 146.637044 330.6505 \nL 146.659341 330.6505 \nL 146.681638 330.6505 \nL 146.703936 330.6505 \nL 146.726233 330.6505 \nL 146.74853 330.6505 \nL 146.770827 330.6505 \nL 146.793125 330.6505 \nL 146.815422 330.6505 \nL 146.837719 330.6505 \nL 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330.6505 \nL 148.732986 330.6505 \nL 148.755283 330.6505 \nL 148.77758 330.6505 \nL 148.799878 330.6505 \nL 148.822175 330.6505 \nL 148.844472 330.6505 \nL 148.866769 330.6505 \nL 148.889067 330.6505 \nL 148.911364 330.6505 \nL 148.933661 330.6505 \nL 148.955958 330.6505 \nL 148.978256 330.6505 \nL 149.000553 330.6505 \nL 149.02285 330.6505 \nL 149.045147 330.6505 \nL 149.067445 330.6505 \nL 149.089742 330.6505 \nL 149.112039 330.6505 \nL 149.134336 330.6505 \nL 149.156634 330.6505 \nL 149.178931 330.6505 \nL 149.201228 330.6505 \nL 149.223525 330.6505 \nL 149.245823 330.6505 \nL 149.26812 330.6505 \nL 149.290417 330.6505 \nL 149.312714 330.6505 \nL 149.335012 330.6505 \nL 149.357309 330.6505 \nL 149.379606 330.6505 \nL 149.401903 330.6505 \nL 149.424201 330.6505 \nL 149.446498 330.6505 \nL 149.468795 330.6505 \nL 149.491093 330.6505 \nL 149.51339 330.6505 \nL 149.535687 330.6505 \nL 149.557984 330.6505 \nL 149.580282 330.6505 \nL 149.602579 330.6505 \nL 149.624876 330.6505 \nL 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597.286875 330.6505 \nL 597.309173 330.6505 \nL 597.33147 330.6505 \nL 597.353767 330.6505 \nL 597.376064 330.6505 \nL 597.398362 330.6505 \nL 597.420659 330.6505 \nL 597.442956 330.6505 \nL 597.465253 330.6505 \nL 597.487551 330.6505 \nL 597.509848 330.6505 \nL 597.532145 330.6505 \nL 597.554442 330.6505 \nL 597.57674 330.6505 \nL 597.599037 330.6505 \nL 597.621334 330.6505 \nL 597.643631 330.6505 \nL 597.665929 330.6505 \nL 597.688226 330.6505 \nL 597.710523 330.6505 \nL 597.73282 330.6505 \nL 597.755118 330.6505 \nL 597.777415 330.6505 \nL 597.799712 330.6505 \nL 597.822009 330.6505 \nL 597.844307 330.6505 \nL 597.866604 330.6505 \nL 597.888901 330.6505 \nL 597.911198 330.6505 \nL 597.933496 330.6505 \nL 597.955793 330.6505 \nL 597.97809 330.6505 \nL 598.000387 330.6505 \nL 598.022685 330.6505 \nL 598.044982 330.6505 \nL 598.067279 330.6505 \nL 598.089576 330.6505 \nL 598.111874 330.6505 \nL 598.134171 330.6505 \nL 598.156468 330.6505 \nL 598.178765 330.6505 \nL 598.201063 330.6505 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330.6505 \nL 599.159845 330.6505 \nL 599.182142 330.6505 \nL 599.204439 330.6505 \nL 599.226736 330.6505 \nL 599.249034 330.6505 \nL 599.271331 330.6505 \nL 599.293628 330.6505 \nL 599.315926 330.6505 \nL 599.338223 330.6505 \nL 599.36052 330.6505 \nL 599.382817 330.6505 \nL 599.405115 330.6505 \nL 599.427412 330.6505 \nL 599.449709 330.6505 \nL 599.472006 330.6505 \nL 599.494304 330.6505 \nL 599.516601 330.6505 \nL 599.538898 330.6505 \nL 599.561195 330.6505 \nL 599.583493 330.6505 \nL 599.60579 330.6505 \nL 599.628087 330.6505 \nL 599.650384 330.6505 \nL 599.672682 330.6505 \nL 599.694979 330.6505 \nL 599.717276 330.6505 \nL 599.739573 330.6505 \nL 599.761871 330.6505 \nL 599.784168 330.6505 \nL 599.806465 330.6505 \nL 599.828762 330.6505 \nL 599.85106 330.6505 \nL 599.873357 330.6505 \nL 599.895654 330.6505 \nL 599.917951 330.6505 \nL 599.940249 330.6505 \nL 599.962546 330.6505 \nL 599.984843 330.6505 \nL 600.00714 330.6505 \nL 600.029438 330.6505 \nL 600.051735 330.6505 \nL 600.074032 330.6505 \nL 600.096329 330.6505 \nL 600.118627 330.6505 \nL 600.140924 330.6505 \nL 600.163221 330.6505 \nL 600.185518 330.6505 \nL 600.207816 330.6505 \nL 600.230113 330.6505 \nL 600.25241 330.6505 \nL 600.274707 330.6505 \nL 600.297005 330.6505 \nL 600.319302 330.6505 \nL 600.341599 330.6505 \nL 600.363897 330.6505 \nL 600.386194 330.6505 \nL 600.408491 330.6505 \nL 600.430788 330.6505 \nL 600.453086 330.6505 \nL 600.475383 330.6505 \nL 600.49768 330.6505 \nL 600.519977 330.6505 \nL 600.542275 330.6505 \nL 600.564572 330.6505 \nL 600.586869 330.6505 \nL 600.609166 330.6505 \nL 600.631464 330.6505 \nL 600.653761 330.6505 \nL 600.676058 330.6505 \nL 600.698355 330.6505 \nL 600.720653 330.6505 \nL 600.74295 330.6505 \nL 600.765247 330.6505 \nL 600.787544 330.6505 \nL 600.809842 330.6505 \nL 600.832139 330.6505 \nL 600.854436 330.6505 \nL 600.876733 330.6505 \nL 600.899031 330.6505 \nL 600.921328 330.6505 \nL 600.943625 330.6505 \nL 600.965922 330.6505 \nL 600.98822 330.6505 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17.8405 \nL 599.873357 17.8405 \nL 599.85106 17.8405 \nL 599.828762 17.8405 \nL 599.806465 17.8405 \nL 599.784168 17.8405 \nL 599.761871 17.8405 \nL 599.739573 17.8405 \nL 599.717276 17.8405 \nL 599.694979 17.8405 \nL 599.672682 17.8405 \nL 599.650384 17.8405 \nL 599.628087 17.8405 \nL 599.60579 17.8405 \nL 599.583493 17.8405 \nL 599.561195 17.8405 \nL 599.538898 17.8405 \nL 599.516601 17.8405 \nL 599.494304 17.8405 \nL 599.472006 17.8405 \nL 599.449709 17.8405 \nL 599.427412 17.8405 \nL 599.405115 17.8405 \nL 599.382817 17.8405 \nL 599.36052 17.8405 \nL 599.338223 17.8405 \nL 599.315926 17.8405 \nL 599.293628 17.8405 \nL 599.271331 17.8405 \nL 599.249034 17.8405 \nL 599.226736 17.8405 \nL 599.204439 17.8405 \nL 599.182142 17.8405 \nL 599.159845 17.8405 \nL 599.137547 17.8405 \nL 599.11525 17.8405 \nL 599.092953 17.8405 \nL 599.070656 17.8405 \nL 599.048358 17.8405 \nL 599.026061 17.8405 \nL 599.003764 17.8405 \nL 598.981467 17.8405 \nL 598.959169 17.8405 \nL 598.936872 17.8405 \nL 598.914575 17.8405 \nL 598.892278 17.8405 \nL 598.86998 17.8405 \nL 598.847683 17.8405 \nL 598.825386 17.8405 \nL 598.803089 17.8405 \nL 598.780791 17.8405 \nL 598.758494 17.8405 \nL 598.736197 17.8405 \nL 598.7139 17.8405 \nL 598.691602 17.8405 \nL 598.669305 17.8405 \nL 598.647008 17.8405 \nL 598.624711 17.8405 \nL 598.602413 17.8405 \nL 598.580116 17.8405 \nL 598.557819 17.8405 \nL 598.535522 17.8405 \nL 598.513224 17.8405 \nL 598.490927 17.8405 \nL 598.46863 17.8405 \nL 598.446333 17.8405 \nL 598.424035 17.8405 \nL 598.401738 17.8405 \nL 598.379441 17.8405 \nL 598.357144 17.8405 \nL 598.334846 17.8405 \nL 598.312549 17.8405 \nL 598.290252 17.8405 \nL 598.267955 17.8405 \nL 598.245657 17.8405 \nL 598.22336 17.8405 \nL 598.201063 17.8405 \nL 598.178765 17.8405 \nL 598.156468 17.8405 \nL 598.134171 17.8405 \nL 598.111874 17.8405 \nL 598.089576 17.8405 \nL 598.067279 17.8405 \nL 598.044982 17.8405 \nL 598.022685 17.8405 \nL 598.000387 17.8405 \nL 597.97809 17.8405 \nL 597.955793 17.8405 \nL 597.933496 17.8405 \nL 597.911198 17.8405 \nL 597.888901 17.8405 \nL 597.866604 17.8405 \nL 597.844307 17.8405 \nL 597.822009 17.8405 \nL 597.799712 17.8405 \nL 597.777415 17.8405 \nL 597.755118 17.8405 \nL 597.73282 17.8405 \nL 597.710523 17.8405 \nL 597.688226 17.8405 \nL 597.665929 17.8405 \nL 597.643631 17.8405 \nL 597.621334 17.8405 \nL 597.599037 17.8405 \nL 597.57674 17.8405 \nL 597.554442 17.8405 \nL 597.532145 17.8405 \nL 597.509848 17.8405 \nL 597.487551 17.8405 \nL 597.465253 17.8405 \nL 597.442956 17.8405 \nL 597.420659 17.8405 \nL 597.398362 17.8405 \nL 597.376064 17.8405 \nL 597.353767 17.8405 \nL 597.33147 17.8405 \nL 597.309173 17.8405 \nL 597.286875 17.8405 \nL 597.264578 17.8405 \nL 597.242281 17.8405 \nL 597.219983 17.8405 \nL 597.197686 17.8405 \nL 597.175389 17.8405 \nL 597.153092 17.8405 \nL 597.130794 17.8405 \nL 597.108497 17.8405 \nL 597.0862 17.8405 \nL 597.063903 17.8405 \nL 597.041605 17.8405 \nL 597.019308 17.8405 \nL 596.997011 17.8405 \nL 596.974714 17.8405 \nL 596.952416 17.8405 \nL 596.930119 17.8405 \nL 596.907822 17.8405 \nL 596.885525 17.8405 \nL 596.863227 17.8405 \nL 596.84093 17.8405 \nL 596.818633 17.8405 \nL 596.796336 17.8405 \nL 596.774038 17.8405 \nL 596.751741 17.8405 \nL 596.729444 17.8405 \nL 596.707147 17.8405 \nL 596.684849 17.8405 \nL 596.662552 17.8405 \nL 596.640255 17.8405 \nL 596.617958 17.8405 \nL 596.59566 17.8405 \nL 596.573363 17.8405 \nL 596.551066 17.8405 \nL 596.528769 17.8405 \nL 596.506471 17.8405 \nL 596.484174 17.8405 \nL 596.461877 17.8405 \nL 596.43958 17.8405 \nL 596.417282 17.8405 \nL 596.394985 17.8405 \nL 596.372688 17.8405 \nL 596.350391 17.8405 \nL 596.328093 17.8405 \nL 596.305796 17.8405 \nL 596.283499 17.8405 \nL 596.261202 17.8405 \nL 596.238904 17.8405 \nL 596.216607 17.8405 \nL 596.19431 17.8405 \nL 596.172012 17.8405 \nL 596.149715 17.8405 \nL 596.127418 17.8405 \nL 596.105121 17.8405 \nL 596.082823 17.8405 \nL 596.060526 17.8405 \nL 596.038229 17.8405 \nL 596.015932 17.8405 \nL 595.993634 17.8405 \nL 595.971337 17.8405 \nL 595.94904 17.8405 \nL 595.926743 17.8405 \nL 595.904445 17.8405 \nL 595.882148 17.8405 \nL 595.859851 17.8405 \nL 595.837554 17.8405 \nL 595.815256 17.8405 \nL 595.792959 17.8405 \nL 595.770662 17.8405 \nL 595.748365 17.8405 \nL 595.726067 17.8405 \nL 595.70377 17.8405 \nL 595.681473 17.8405 \nL 595.659176 17.8405 \nL 595.636878 17.8405 \nL 595.614581 17.8405 \nL 595.592284 17.8405 \nL 595.569987 17.8405 \nL 595.547689 17.8405 \nL 595.525392 17.8405 \nL 595.503095 17.8405 \nL 595.480798 17.8405 \nL 595.4585 17.8405 \nL 595.436203 17.8405 \nL 595.413906 17.8405 \nL 595.391609 17.8405 \nL 595.369311 17.8405 \nL 595.347014 17.8405 \nL 595.324717 17.8405 \nL 595.30242 17.8405 \nL 595.280122 17.8405 \nL 595.257825 17.8405 \nL 595.235528 17.8405 \nL 595.21323 17.8405 \nL 595.190933 17.8405 \nL 595.168636 17.8405 \nL 595.146339 17.8405 \nL 595.124041 17.8405 \nL 595.101744 17.8405 \nL 595.079447 17.8405 \nL 595.05715 17.8405 \nL 595.034852 17.8405 \nL 595.012555 17.8405 \nL 594.990258 17.8405 \nL 594.967961 17.8405 \nL 594.945663 17.8405 \nL 594.923366 17.8405 \nL 594.901069 17.8405 \nL 594.878772 17.8405 \nL 594.856474 17.8405 \nL 594.834177 17.8405 \nL 594.81188 17.8405 \nL 594.789583 17.8405 \nL 594.767285 17.8405 \nL 594.744988 17.8405 \nL 594.722691 17.8405 \nL 594.700394 17.8405 \nL 594.678096 17.8405 \nL 594.655799 17.8405 \nL 594.633502 17.8405 \nL 594.611205 17.8405 \nL 594.588907 17.8405 \nL 594.56661 17.8405 \nL 594.544313 17.8405 \nL 594.522016 17.8405 \nL 594.499718 17.8405 \nL 594.477421 17.8405 \nL 594.455124 17.8405 \nL 594.432827 17.8405 \nL 594.410529 17.8405 \nL 594.388232 17.8405 \nL 594.365935 17.8405 \nL 594.343638 17.8405 \nL 594.32134 17.8405 \nL 594.299043 17.8405 \nL 594.276746 17.8405 \nL 594.254449 17.8405 \nL 594.232151 17.8405 \nL 594.209854 17.8405 \nL 594.187557 17.8405 \nL 594.165259 17.8405 \nL 594.142962 17.8405 \nL 594.120665 17.8405 \nL 594.098368 17.8405 \nL 594.07607 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "visualise.plot_events(\n",
    "    eeg_timestamps,\n",
    "    event_timeseries,\n",
    "    fs,\n",
    "    label2code\n",
    ")"
   ]
  },
  {
   "source": [
    "# Preprocess EEG data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 1. Rereference the EEG data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rereference according to the mean\n",
    "eeg_data = eeg_data - np.mean(eeg_data, axis=0)"
   ]
  },
  {
   "source": [
    "## 2. Filter EEG data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lf = 8\n",
    "hf = 16\n",
    "nf = 50\n",
    "\n",
    "# Design notch filter\n",
    "b, a = signal.iirnotch(w0=nf, Q=7, fs=fs)\n",
    "freq, h = signal.freqz(b, a, fs = fs)\n",
    "\n",
    "# Apply notch and bandpass filters\n",
    "eeg_notched = signal.filtfilt(b, a, eeg_data, axis=1)\n",
    "eeg_notched_bp = preprocess.bandpass(eeg_notched, lf=lf, hf=hf, fs=fs, ftype='butter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.title(f'Filtering on all channels')\n",
    "\n",
    "for i in range(eeg_data.shape[0]):\n",
    "    plt.psd(eeg_notched_bp[i], Fs=fs, label=f'{preprocess.channel2name(i)}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='lower left', shadow=True)\n",
    "plt.xlim([0,60])\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 3. Normalise EEG data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_notched_bp_norm = preprocess.normalise_eeg(eeg_notched_bp)"
   ]
  },
  {
   "source": [
    "# Split into trials"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sample rate: 250\nNumber of EEG channels: 8\nChannel names: ['Fz', 'C3', 'Cz', 'C4', 'Pz', 'PO7', 'Oz', 'PO8']\n\nNumber of classes: 4\nClass labels: ['imagery_handL', 'imagery_handR', 'cue_handL', 'cue_handR']\n\nNumber of events: 1213\nAll event codes: [0 1 2 3 4 5 6 7]\nClass codes: [7, 5, 6, 4]\n"
     ]
    }
   ],
   "source": [
    "EEG = eeg_notched_bp_norm.copy()\n",
    "event_onsets = event_timestamps.copy()\n",
    "classes = ['imagery_handL', 'imagery_handR', 'cue_handL', 'cue_handR']\n",
    "class_codes = [label2code[code] for code in classes]\n",
    "nclasses = len(classes)\n",
    "nchannels = EEG.shape[0]\n",
    "\n",
    "# Print some information\n",
    "print('Sample rate:', fs)\n",
    "print('Number of EEG channels:', nchannels)\n",
    "print('Channel names:', [preprocess.channel2name(channel) for channel in range(EEG.shape[0])])\n",
    "\n",
    "print('\\nNumber of classes:', nclasses)\n",
    "print('Class labels:', classes)\n",
    "\n",
    "print('\\nNumber of events:', len(event_onsets))\n",
    "print('All event codes:', np.unique(event_codes))\n",
    "print('Class codes:', class_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "imagery_handL: (8, 500, 200)\nimagery_handR: (8, 500, 200)\ncue_handL: (8, 375, 200)\ncue_handR: (8, 375, 200)\n"
     ]
    }
   ],
   "source": [
    "mi_window = [0.5, 2.5]\n",
    "mi_nsamples = len(np.arange(int(mi_window[0]*fs), int(mi_window[1]*fs)))\n",
    "\n",
    "idle_window = [0.25, 1.75]\n",
    "idle_nsamples = len(np.arange(int(idle_window[0]*fs), int(idle_window[1]*fs)))\n",
    "\n",
    "class_nsamples = [mi_nsamples, mi_nsamples, idle_nsamples, idle_nsamples]\n",
    "\n",
    "trials = get_trials(\n",
    "    eeg_data=EEG,\n",
    "    event_time_series=event_timeseries,\n",
    "    class_labels=classes,\n",
    "    label2code=label2code,\n",
    "    nsamples=class_nsamples\n",
    ")\n",
    "\n",
    "for key, val in trials.items():\n",
    "    print(f'{key}: {val.shape}')"
   ]
  },
  {
   "source": [
    "# Some statistical Analysis to remove noisy trials"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x360 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "fig.suptitle('Unfiltered Trials')\n",
    "\n",
    "axs[0].set_title('Maximum values')\n",
    "axs[0].boxplot([ trials[cl].max(axis = (0,1)) for cl in classes ], labels=classes)\n",
    "\n",
    "axs[1].set_title('Standard deviations')\n",
    "axs[1].boxplot([ trials[cl].std(axis = (0,1)) for cl in classes ], labels=classes)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'imagery_handL': 0.5966861295360828,\n",
       " 'imagery_handR': 0.5607748092425172,\n",
       " 'cue_handL': 0.5647089798842686,\n",
       " 'cue_handR': 0.5537755257558372}"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# Set the upper limit on the maxval and std dev as 2*(3rd quartile)\n",
    "\n",
    "upperlimit_max = {}\n",
    "for cl in classes:\n",
    "    upperlimit_max[cl] = np.percentile(trials[cl].max(axis = (0,1)), [75])[0] * 1.5\n",
    "\n",
    "upperlimit_std = {}\n",
    "for cl in classes:\n",
    "    upperlimit_std[cl] = np.percentile(trials[cl].std(axis = (0,1)), [75])[0] * 1.5\n",
    "\n",
    "upperlimit_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "imagery_handL has (8, 500, 187) non noisy trials\nimagery_handR has (8, 500, 190) non noisy trials\ncue_handL has (8, 375, 189) non noisy trials\ncue_handR has (8, 375, 192) non noisy trials\n"
     ]
    }
   ],
   "source": [
    "good_trials = get_good_trials(\n",
    "    trials=trials,\n",
    "    nchannels=nchannels,\n",
    "    class_nsamples=class_nsamples,\n",
    "    upperlimit_std=upperlimit_std,\n",
    "    upperlimit_max=upperlimit_max\n",
    ")\n",
    "\n",
    "for cl in classes:\n",
    "    print(f'{cl} has {good_trials[cl].shape} non noisy trials')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x360 with 2 Axes>",
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c7oyCJEmSNHE4gixJkiRVmCBLkiRJFSbIkiRJUoUJsiRJklRxcKMDGMyRRx6Zxx57bKPDkKQxc/vttz+YmTMaHcdQ7IclTQRD9cVNmSAfe+yxbNw41IphktT6ImJ/dxVrOPthSRPBUH2xUywkSZKkChNkSZIkqcIEWZIkSaowQZYkSZIqTJAlSZKkChNkSZIkqcIEWZIkSaowQZYkSZIqTJBrtHbtWubPn8+kSZOYP38+a9eubXRIkjSh2A9LGi9NeSe9ZrN27VqWLl3K6tWrOemkk9iwYQOLFy8GoLOzs8HRSVL7sx+WNJ4iMxsdw1N0dHRkM93idP78+Zx++ulce+21bNq0iTlz5ux73dvb2+jwJLWgiLg9MzsaHcdQmrEfXrlyJQsWLNi3raenh66uLvthSaM2VF/sCHIN7rrrLnbs2EF3d/e+kYtFixZx332D3r5bklRnmzZt4qSTTuq37aSTTmLTpk0NikhSO3MOcg2mTJlCV1cXCxYsYPLkySxYsICuri6mTJnS6NAkaUKYM2cOGzZs6Ldtw4YNzJkzp0ERSWpnJsg12LVrF5dffjk9PT3s3r2bnp4eLr/8cnbt2tXo0CRpQli6dCmLFy/u1w8vXryYpUuXNjo0SW3IKRY1mDt3LscddxwLFy7k8ccf52lPexoLFy7k0EMPbXRokjQh9F2I19XVte9akOXLl3uBnqQx4QhyDRYsWMC6detYsWIFO3bsYMWKFaxbt67fxSKSpLHV2dlJb28ve/fupbe31+RY0pgxQa5BT08PS5Ysobu7m+nTp9Pd3c2SJUvo6elpdGhNz3VLJUlSqzFBrsGmTZu46KKL+o1cXHTRRV49PYy+dUtXrlzJzp07WblyJUuXLjVJlkYhIk6NiB9HxN0R8b5Byk+OiN9ExPfLxwcqZfdGxA/L7c2zdtsI+YNb0ngZNkGOiKMjoiciNkXEnRHx7kHq7K9j3m+n3gq8enp0li9fzurVq/ut/rF69WqWL1/e6NCklhIRk4BPAAuBuUBnRMwdpOo3MvOE8vHBAWULyu1Nu/by/viD+8D440Iaoczc7wN4HvCi8vl04CfA3AF1TgbWDbLvJOCnwH8ApgB3DNx3sMeLX/zibCZr1qzJ2bNn5/r163PXrl25fv36nD17dq5Zs6bRoTW1gw46KHft2tVv265du/Kggw5qUERS8wA25jB9YT7Zl74c+HLl9fuB92cN/XBZdi9wZK3vl03YD8+bNy/Xr1/fb9v69etz3rx5DYqodfgdJg1tqL542BHkzPxlZn63fL4d2AQcVWP+fSJwd2bek5m7gKuB02rct2l0dnayfPlyurq6mDp1Kl1dXV49XQNH3qW6OQq4v/J6M4P3wy+PiDsi4saImFfZnsBNEXF7RJw7loGOFW8UMnqezZNGbkRzkCPiWOAPgW8PUjxYx1xrp970vHp65Fy3VKqbGGRbDnj9XeCYzDweWAlcWyl7RWa+iGKKxrsi4pWDvknEuRGxMSI2bt26tQ5h148/uEfPHxfSyNWcIEfEYcAXgL/MzEcHFA/VMdfSqfcdv2k7ZnD+1mg48i7VzWbg6MrrmcCWaoXMfDQzHyuf3wBMjogjy9dbyj8fAK6hOLv3FJl5RWZ2ZGbHjBkz6v8pDoA/uEfPHxfSyNV0o5CImEyRHH8mM784sLyaMGfmDRHxybJjHrZTr+x3BXAFQEdHx6BJdKP0XRyyevVqTjrpJDZs2MDixYsBTPaG0dnZaRtJB+424LiImA38AjgLOLtaISKeC/w6MzMiTqQYAHkoIqYBB2Xm9vL5KcDAC/iaXmdnJ7fccku/Gza94x3vsH+pwdKlSznzzDOZNm0aP//5z5k1axY7duzgsssua3RoUtOqZRWLAFYDmzLzo0PUeW5Zj2rHTKVTj4gpFJ36dfUKfrw4f0tSI2XmHuB84MsU14F8LjPvjIjzIuK8stoZQG9E3AF8HDirvADlOcCGcvt3gOsz80vj/ykOzNq1a7n++uu58cYb2bVrFzfeeCPXX3+9Z/NGqPgnIWk4Mdx/log4CfgG8EPgiXLzhcAsgMxcFRHnA+8E9gC/A/4qM28p938t8DGKFS26M3PYrLKjoyM3bmyepTonTZrEzp07mTx58r5tu3fvZurUqezdu7eBkUlqVRFxezbxkmvN1g/Pnz+flStX9ruDaU9PD11dXfT29jYwsuZn20lDG6ovHjZBbgQ7ZkntzgR5ZByoGD3bThraUH2xd9KrgReHSFJjeaHZ6Nl20siZINfA1RhGz9U/JNWDAxWjZ9tJI1fTKhZyNYbRcPUPSfXS12d0dXWxadMm5syZ40BFjWw7aeScg6wx49xtaWjOQZakxnMOssadd2+SJEmtyARZY2bOnDksW7as3xzkZcuWeWGIJElqaibIGjMLFizgkksuYdGiRWzfvp1FixZxySWX9JtyIUmS1GxMkDVmenp6WLJkCd3d3UyfPp3u7m6WLFlCT09Po0OTJKkmrsY0Oq3ebq5ioTGzadMmvve97/GhD31o37bdu3fz4Q9/uIFRSZJUG1djGp12aDdHkDVmXJxektTKli9fzurVq1mwYAGTJ09mwYIFrF69muXLlzc6tKbWDu1mgqwx4+L0kqRW5mpMo9MO7eYUC40ZF6eXJLWyvjOh1YvLPRM6vHZoN0eQNaY6Ozvp7e1l79699Pb2mhxLklqGZ0JHpx3azRFkjam1a9eyfPnyfSPIS5cuNUmWJLUEz4SOTju0mwmyxkw7XMUqSZrYOjs7/c6agJxioTHTDlexSpKkkekbIFu5ciU7d+5k5cqVLF26tKXWQo7MbHQMT9HR0ZEbN25sdBg6QJMmTWLnzp1Mnjx537bdu3czdepU9u7d28DIpMaLiNszs6PRcQzFfljSaM2fP5+VK1f2u0ivp6eHrq4uent7GxjZUw3VFzuCrDEzZ84cli1b1u9OOsuWLWupq1glSdLItMMybybIGjMLFizgkksuYdGiRWzfvp1FixZxySWX9PtFKUmS2ks73CjMBFljpqenhyVLltDd3c306dPp7u5myZIl9PT0NDo0SZI0RtphmTfnIGvMOAdZGppzkEfOZSOl1tEq/1+H6otd5k1jph3upCOpObhspNRaWn15PKdYaMy0wykWSc3BZSMljSdHkCsiom7HasapK+OtHe6kI6k5tMNV8ZJahwlyRS1JbUSY/I5Aq59ikdQcnLIlaTw5xUKS1PScsiVpPDmCLElqek7ZkjSehk2QI+Jo4CrgucATwBWZedmAOucAS8qXjwHvzMw7yrJ7ge3AXmBPMy9rJElqXk7ZkjReaplisQe4IDPnAC8D3hURcwfU+Rnwqsx8IfC3wBUDyhdk5gkmx1Lt1q5d2+823WvXrm10SFJD+X9C0ngZdgQ5M38J/LJ8vj0iNgFHAXdV6txS2eVWYGad45QmFNd8lfpbu3Yt7373u5k2bRqZyY4dO3j3u98N+H9CUv2N6CK9iDgW+EPg2/upthi4sfI6gZsi4vaIOHc/xz43IjZGxMatW7eOJCyp7Sxfvpzjjz+ehQsXMmXKFBYuXMjxxx/vmq8TWEScGhE/joi7I+J9g5SfHBG/iYjvl48P1LpvK3jve9/LpEmT6O7u5vHHH6e7u5tJkybx3ve+t9GhSWpDNSfIEXEY8AXgLzPz0SHqLKBIkJdUNr8iM18ELKSYnvHKwfbNzCsysyMzO2bMmFHzB5Da0Z133sm6detYsWIFO3bsYMWKFaxbt44777yz0aGpASJiEvAJin50LtA5yFQ3gG+U09lOyMwPjnDfprZ582auuuqqfjcKueqqq9i8eXOjQ5PUhmpKkCNiMkVy/JnM/OIQdV4IfAo4LTMf6tuemVvKPx8ArgFOPNCgpXYXEbzqVa+iu7ub6dOn093dzate9aq63sxGLeVE4O7MvCczdwFXA6eNw76SNCENmyBH8Y28GtiUmR8dos4s4IvAmzPzJ5Xt0yJiet9z4BSgtx6BS+0sM/nqV7/Kgw8+SGby4IMP8tWvftWb1ExcRwH3V15vLrcN9PKIuCMiboyIeSPct6mnus2cOZO3vvWt/dZBfutb38rMmV7yIqn+ahlBfgXwZuDVlbltr42I8yLivLLOB4BnAZ8syzeW258DbIiIO4DvANdn5pfq/SGkdjR16lQOOeQQAA455BCmTp3a4IjUQIOdOhj4a+m7wDGZeTywErh2BPsWG5t4qtull17Knj17WLRoEVOnTmXRokXs2bOHSy+9tNGhSWpDtaxisYHBO9hqnbcDbx9k+z3A8aOOTi2jXqf+HSF90u7du+nq6uK8885j1apVXow0sW0Gjq68nglsqVaoXhuSmTdExCcj4sha9m0FfStV9F2oOm3aNFasWOEKFpLGhLeaVl1k5n4ftdQxOe7vda97HRdeeCHTpk3jwgsv5HWve12jQ2oZbbhe7m3AcRExOyKmAGcB11UrRMRzyylxRMSJFP37Q7Xs2yo6Ozvp7e1l79699Pb2mhxLGjPealpqQjNnzuS2227jxhtv3LcO8jnnnON8yxq04xrSmbknIs4HvgxMAroz886+aW6ZuQo4A3hnROwBfgeclcWvzkH3bcgHqZFnpCQ1WjRjB9LR0ZEbN24cvmIDRISd7ijYbiNTvSnCz3/+c2bNmsWOHTu47LLLWjbJGy/z589n5cqVLFiwYN+2np4eurq66O1tnmuEI+L2Zr67qP2wpIlgqL7YKRZSE+rs7OSyyy5j2rRpQDHf0uS4Nps2beKkk07qt+2kk05i06ZNDYpIktRqTJClJuV8y9GZM2cOGzZs6Ldtw4YNzJkzp0ERSY3XhvPypTFlgiyprSxdupQzzzyT2bNnc9BBBzF79mzOPPNMli5d2ujQpIbom5e/cuVKdu7cycqVK1m6dKlJco26urqYOnUqEcHUqVPp6upqdEgaBybIktqWdx6UiqXxVq9e3e823atXr963ZJ6G1tXVxapVq1ixYgU7duxgxYoVrFq1yiR5AvAivRHy4pDRsd00XrxIrz7sh9vHpEmT2LlzJ5MnT963bffu3UydOpW9e/c2MLLmN3XqVFasWMFf/dVf7dv20Y9+lAsvvJCdO3c2MDLVixfpSZoQvEhP6s95+aP3+OOPc8QRR/Sbv33EEUfw+OOPNzo0jTHXQZbUVvqSgeoIssmAJrK+efnTpk3jvvvu45hjjtm3bKT27+CDD+aCCy7g85///L511c844wwOPtj0qd05giyprSxdupTFixfT09PD7t276enpYfHixV6kJ+G8/JE6/PDD2bZtG2effTZTp07l7LPPZtu2bRx++OGNDk1jzJ9AktpK33J4XV1dbNq0iTlz5rB8+XKXydOEtXz5cj772c8OOi/f/xf7t23bNg477DAeeughnnjiCR566CEOO+wwtm3b1ujQNMYcQZbUdlxDWnqS8/JHb8qUKVx88cXs2rWLzGTXrl1cfPHFTJkypdGhaYyZIEtqO94UQXqSF+mN3q5du7j88sv7Tdm6/PLL2bVrV6ND0xgzQZbUVrwpgtSf8/JHb+7cuZx99tn7bhbS1dXF2Wefzdy5cxsdWtNr+YGKzGy6x4tf/OJsVkWTaaRst8EBdXuoMG/evFy/fn2/bevXr8958+Y1KKLBARuzCfrboR72w+1lzZo1OW/evDzooINy3rx5uWbNmkaH1BLWrFmTs2fPzvXr1+euXbty/fr1OXv2bNtvGK3UbkP1xV6kJzVQ1nCzA2+KMDLOt5RUL170OzrVuzcC++7e2EoXhpogS2orroMs9dc37Wj16tX71vJdvHgxQMskK43U2dlpO41QOwxUOAdZUltxvqXU3/LlywedR7t8+fJGh6Y21Q4XhjqCLKmtdHZ2csstt7Bw4UIef/xxnva0p/GOd7zDESBNWHfddRe//e1vnzKCfO+99zY6NLWpvoGKgf/mWulHmSPIktrK2rVruf7667nxxhvZtWsXN954I9dff33rXUEt1cmUKVM4//zzWbBgAZMnT2bBggWcf/75ruWrMdPZ2cny5cv7nbVotbnb0YwX/3R0dOTGjRsbHcagvGBqdGy30bPtRmb+/PmcfvrpXHvttfsuqul73dvb2+jw9omI2zOzo9FxDMV+uH0cdNBBHHPMMXR3d+8bzVu0aBH33XcfTzzxRKPDkxpqqL7YKRaS2spdd93Fjh07Bk0GpIlo7ty5HHfccf2mHS1cuJBp06Y1OjSpaTnFQlJbmTJlCl1dXf1OJ3d1dXk6WRPWggULWLduHStWrGDHjh2sWLGCdevW9VvpRaq3Vr9RiAmypLaya9cuPvKRjzB79mwmTZrE7Nmz+chHPuKtYTVh9fT0sGTJErq7u5k+fTrd3d0sWbKEnp6eRoemNtUOdzR1DvIIOfdtdGy30bPtRuboo4/mscce4xnPeAb33XcfxxxzDI888giHHXYY999/f6PD28c5yKPn/4mRmTRpEjt37mTy5Mn7tu3evZupU6eyd+/eBkamdjV//nxWrlzZ7yxFT08PXV1dTXUtCAzdFw87ghwRR0dET0Rsiog7I+Ldg9SJiPh4RNwdET+IiBdVyk6NiB+XZe878I8iSfs3depUuru7efzxx+nu7mbq1KmNDklqmHZYk1atZaLcKGQPcEFmzgFeBrwrIuYOqLMQOK58nAv8I0BETAI+UZbPBToH2VeS6mbLli1ceuml/ZYXuvTSS9myZUujQ5MawpvnaLzNmTOHZcuW9ZuDvGzZspb6UTbsKhaZ+Uvgl+Xz7RGxCTgKuKtS7TTgqizOed0aEc+IiOcBxwJ3Z+Y9ABFxdVm3uq8k1c2cOXOYOXNmv9N4PT09LdUxS/XUt/ZsV1fXvqUPW21NWrWWBQsWcMkll3DJJZdw3nnnsWrVKpYsWcJ5553X6NBqNqJl3iLiWOAPgW8PKDoKqE7u21xuG2z7S0ccpSQNEBFDlr361a8e0T7OZ1W76+zsNCHWuKleGPqe97yHOXPmsGTJEq699tpGh1azmlexiIjDgC8Af5mZjw4sHmSX3M/2wY5/bkRsjIiNW7durTUsSRNUZg75WLNmDfPmzQNg3rx5rFmzZr/1JUn1s2nTJi666CJ6e3vZu3cvvb29XHTRRW03B5mImEyRHH8mM784SJXNwNGV1zOBLfvZ/hSZeUVmdmRmx4wZM2oJS5IG1dnZuW+KRW9vryNnkjSO2uHC0FpWsQhgNbApMz86RLXrgLeUq1m8DPhNOXf5NuC4iJgdEVOAs8q6kiRJakPtcGFoLXOQXwG8GfhhRHy/3HYhMAsgM1cBNwCvBe4Gfgv8RVm2JyLOB74MTAK6M/POen4ASZoIIuJU4DKKvvRTmfmRIeq9BLgVODMzP19uuxfYDuwF9jTz+suSWl87XBhayyoWGxh8LnG1TgLvGqLsBooEWpI0CpUlM19DMXXttoi4LjPvGqTeJRSDEgMtyMwHxzxYSaL1Lwz1VtOS1PxOpFwyMzN3AX1LZg7URXG9yAPjGZwktRsTZElqfkMtpblPRBwFvBFYNcj+CdwUEbdHxLljFqUktYkRrYMsSWqIWpbM/BiwJDP3DrLe8ysyc0tEPBv4SkT8KDNvfsqbFMnzuQCzZs068KilFrG/ddVHyqUj24MJsiQ1v1qWzOwAri6/6I8EXhsRezLz2szcApCZD0TENRRTNp6SIGfmFcAVAB0dHX7La8KoJamNCJPfAdr5h4UJsiQ1v31LZgK/oFgy8+xqhcyc3fc8Iq4E1mXmtRExDTgoM7eXz08BPjhukUtqW+38w8IEWZKa3FBLZkbEeWX5YPOO+zwHuKYc6TkYWJOZXxrrmCWplZkgS1ILGGzJzKES48x8W+X5PcDxYxqcJLUZV7GQJEmSKkyQJUmSpAoTZEmSJKnCBFmSJEmq8CI9SZLaQDuvSSuNNxNkSZLaQDuvSSuNN6dYSJIkSRUmyJIkSVKFCbIkSZJUYYIsSZIkVZggS5IkSRUmyJIkSVKFCbIkSZJUYYIsSZIkVZggS5IkSRUmyJIkSVKFCbIkSZJUYYIsSZIkVZggS5IkSRUmyJIkSVLFwcNViIhu4PXAA5k5f5Dy9wDnVI43B5iRmQ9HxL3AdmAvsCczO+oVuCRJkjQWahlBvhI4dajCzPy7zDwhM08A3g98PTMfrlRZUJabHEuSJKnpDZsgZ+bNwMPD1St1AmsPKCJJkiSpgeo2BzkiDqUYaf5CZXMCN0XE7RFx7jD7nxsRGyNi49atW+sVliRJkjQi9bxI7w3ANwdMr3hFZr4IWAi8KyJeOdTOmXlFZnZkZseMGTPqGJYkSZJUu3omyGcxYHpFZm4p/3wAuAY4sY7vJ0mSJNVdXRLkiHg68Crgf1e2TYuI6X3PgVOA3nq8nyRJkjRWalnmbS1wMnBkRGwGLgImA2TmqrLaG4GbMnNHZdfnANdERN/7rMnML9UvdEmSJKn+hk2QM7OzhjpXUiwHV912D3D8aAOTJEmSGsE76UmSJEkVJsiSJElShQmyJLWAiDg1In4cEXdHxPv2U+8lEbE3Is4Y6b6SpIIJsiQ1uYiYBHyCYk35uUBnRMwdot4lwJdHuq8k6UkmyJLU/E4E7s7MezJzF3A1cNog9boo7mb6wCj2lSSVJkyCfMQRRxARB/wA6nKcI444osEtIqmFHAXcX3m9udy2T0QcRbHk5ir6G3ZfSVJ/wy7z1i62bdtGZjY6jH36km1JqsFgHcbADu1jwJLM3Dugf6ll36JixLnAuQCzZs0aeZSS1CYmTIIsSS1sM3B05fVMYMuAOh3A1WVyfCTw2ojYU+O+AGTmFcAVAB0dHc0zoiBJ48wEWZKa323AcRExG/gFcBZwdrVCZs7uex4RVwLrMvPaiDh4uH0lSf2ZIEtSk8vMPRFxPsXqFJOA7sy8MyLOK8sHzjsedt/xiFuSWpUJsiS1gMy8AbhhwLZBE+PMfNtw+0qShjZhVrGQJEmSamGCLEmSJFWYIGtY9VhDGlw/WpIktQbnIGtYzbSGtOtHS5KkseYIsiRJklRhgixJkiRVmCBLksaN1zRIagXOQZYkjRuvaZDUChxBliRJkipMkCVJkqQKE2RJkiSpwgRZkiRJqjBBliRJkipMkKUxUo/lrFzSSpKk8ecyb9IYaablrMAlrSRJqtWwI8gR0R0RD0RE7xDlJ0fEbyLi++XjA5WyUyPixxFxd0S8r56BS5IkSWOhlikWVwKnDlPnG5l5Qvn4IEBETAI+ASwE5gKdETH3QIKVJEmSxtqwCXJm3gw8PIpjnwjcnZn3ZOYu4GrgtFEcR5IkSRo39bpI7+URcUdE3BgR88ptRwH3V+psLrdJkiRJTaseF+l9FzgmMx+LiNcC1wLHAYNdETTkFUsRcS5wLsCsWbPqEJYkSZI0cgc8gpyZj2bmY+XzG4DJEXEkxYjx0ZWqM4Et+znOFZnZkZkdM2bMONCwJEmSpFE54AQ5Ip4b5fpREXFiecyHgNuA4yJidkRMAc4CrjvQ95MkSapVM61J73r0rWPYKRYRsRY4GTgyIjYDFwGTATJzFXAG8M6I2AP8Djgri8Vf90TE+cCXgUlAd2beOSafQlJbOeKII9i2bVtdjnWg6z8/85nP5OGHR3OdsqRm0Exr0rfaevT16ovr8bnHuy8eNkHOzM5hyi8HLh+i7AbghtGFJmmi8gtNkhpvIvfF3mpakqQm10zTBJwqoInAW01LktTkmmkkDzyzovbnCLIkSZJUYYIsSZIkVZggS5IkSRUmyJIkSVKFCbIkSZJUMWFWsciLDoeLn97oMPbJiw5vdAiSWkhEnApcRnHjpU9l5kcGlJ8G/C3wBLAH+MvM3FCW3QtsB/YCezKzYxxDl6SWM2ES5Fj2aNMtkZMXNzoKSa0gIiYBnwBeA2wGbouI6zLzrkq1rwLXZWZGxAuBzwEvqJQvyMwHxy1oSWphTrGQpOZ3InB3Zt6TmbuAq4HTqhUy87F8chRgGtA8IwKS1GJMkCWp+R0F3F95vbnc1k9EvDEifgRcDyyqFCVwU0TcHhHnjmmkktQGTJAlqfkNdtuyp4wQZ+Y1mfkC4HSK+ch9XpGZLwIWAu+KiFcO+iYR50bExojYuHXr1jqELUmtacLMQZakFrYZOLryeiawZajKmXlzRPx+RByZmQ9m5pZy+wMRcQ3FlI2bB9nvCuAKgI6OjjGZotFMF0x7sbSkoZggS1Lzuw04LiJmA78AzgLOrlaIiOcDPy0v0nsRMAV4KCKmAQdl5vby+SnAB8c3/EqcTXTBtBdLSxqKCbIkNbnM3BMR5wNfpljmrTsz74yI88ryVcCfAW+JiN3A74Azy2T5OcA1EQFFn78mM7/UkA8iSS3CBFmSWkBm3gDcMGDbqsrzS4BLBtnvHuD4MQ9QktqIF+lJkiRJFY4ga1heVCNJkiYSE2QNy4tqJEnSROIUC0mSJKnCBFmSJEmqMEGWJEmSKkyQJUmSpAoTZEmSJKnCBFmSJEmqcJk3SZKaXDOtRw+uSa/2Z4IsjRG/0CTVSzOtRw+ttSZ9M/XF9sOtY9gEOSK6gdcDD2Tm/EHKzwGWlC8fA96ZmXeUZfcC24G9wJ7M7KhT3FLT8wtNkhqvmfriVuuHJ/KPi1pGkK8ELgeuGqL8Z8CrMnNbRCwErgBeWilfkJkPHlCUkiRJGlcT+cfFsAlyZt4cEcfup/yWystbgZl1iEvSBDaRRy0kSY1X7znIi4EbK68TuCkiEvinzLxiqB0j4lzgXIBZs2bVOSxJrWQij1pIkhqvbglyRCygSJBPqmx+RWZuiYhnA1+JiB9l5s2D7V8mz1cAdHR0NMc3oyRJkiacuqyDHBEvBD4FnJaZD/Vtz8wt5Z8PANcAJ9bj/SRJkqSxcsAJckTMAr4IvDkzf1LZPi0ipvc9B04Beg/0/SRJkqSxVMsyb2uBk4EjI2IzcBEwGSAzVwEfAJ4FfDIi4Mnl3J4DXFNuOxhYk5lfGoPPULMylqbwzGc+s9EhSJIkaRC1rGLROUz524G3D7L9HuD40YdWX/W64CcimubiIUmSJNVfXeYgS5IkSe3CW02rJs0yPcWpKZIkaayZIGtY9ZhS4tQUSZLUKpxiIUmSJFWYIEuSJEkVJsiSJElShXOQJUnjyot+JTU7E2RJ0rjxol9JrcApFpIkSVKFCbIkSZJU4RQLaQw1y1xLcL6lJGnkmuV7bLy/w0yQpTFSrzmSE3W+5UTtlIcSEacClwGTgE9l5kcGlJ8G/C3wBLAH+MvM3FDLvpI0mIl8zYAJsqSm44+L/iJiEvAJ4DXAZuC2iLguM++qVPsqcF1mZkS8EPgc8IIa95Xamj+4NVImyJLU/E4E7s7MewAi4mrgNGBfkpuZj1XqTwOy1n3VGpolyYPWSvT8wa3RMEGWpOZ3FHB/5fVm4KUDK0XEG4EPA88GXjeSfcv9zwXOBZg1a9YBB636McmTxperWEhS8xts6PApWU5mXpOZLwBOp5iPXPO+5f5XZGZHZnbMmDFjtLFKUsszQZak5rcZOLryeiawZajKmXkz8PsRceRI95UkmSBLUiu4DTguImZHxBTgLOC6aoWIeH6Uk1Qj4kXAFOChWvaVJPXnHGRJanKZuScizge+TLFUW3dm3hkR55Xlq4A/A94SEbuB3wFnZjHZdNB9G/JBJKlFmCBLUgvIzBuAGwZsW1V5fglwSa37SpKG5hQLSZIkqcIEWZIkSaowQZYkSZIqTJAlSZKkChNkSZIkqcIEWZIkSaoYNkGOiO6IeCAieocoj4j4eETcHRE/KBeo7ys7NSJ+XJa9r56BS5IkSWOhlhHkK4FT91O+EDiufJwL/CNAREwCPlGWzwU6I2LugQQrSZIkjbVhE+TMvBl4eD9VTgOuysKtwDMi4nnAicDdmXlPZu4Cri7rSpIkSU2rHnOQjwLur7zeXG4barskSZLUtOqRIMcg23I/2wc/SMS5EbExIjZu3bq1DmFJkiRJI1ePBHkzcHTl9Uxgy362Dyozr8jMjszsmDFjRh3CkiRJkkauHgnydcBbytUsXgb8JjN/CdwGHBcRsyNiCnBWWVeSJElqWgcPVyEi1gInA0dGxGbgImAyQGauAm4AXgvcDfwW+IuybE9EnA98GZgEdGfmnWPwGSRJkqS6GTZBzszOYcoTeNcQZTdQJNCSJElSS/BOepIkSVKFCbIkSZJUYYIsSZIkVZggS5IkSRUmyJIkSVKFCbIkSZJUMewyb5LGTsRgd2QfXb1ixUVJknSgTJClBjKplSSp+TjFQpIkSaowQZYkSZIqTJAlSZKkChNkSZIkqcIEWZIkSaowQZYkSZIqXOZNUktyDWlJ0lgxQZbUkkxqJUljxSkWktQCIuLUiPhxRNwdEe8bpPyciPhB+bglIo6vlN0bET+MiO9HxMbxjXzkImK/j1rq1HqGQZIG4wiyJDW5iJgEfAJ4DbAZuC0irsvMuyrVfga8KjO3RcRC4ArgpZXyBZn54LgFfQA8O6Dx5pQtDWSCLEnN70Tg7sy8ByAirgZOA/YlyJl5S6X+rcDMcY1QamEmtRrIKRaS1PyOAu6vvN5cbhvKYuDGyusEboqI2yPi3KF2iohzI2JjRGzcunXrAQUsSa3MEWRJan6DndcddMgrIhZQJMgnVTa/IjO3RMSzga9ExI8y8+anHDDzCoqpGXR0dDikJmnCcgRZkprfZuDoyuuZwJaBlSLihcCngNMy86G+7Zm5pfzzAeAaiikbkqQhmCBLUvO7DTguImZHxBTgLOC6aoWImAV8EXhzZv6ksn1aREzvew6cAvSOW+SS1IKcYiFJTS4z90TE+cCXgUlAd2beGRHnleWrgA8AzwI+WV5pvyczO4DnANeU2w4G1mTmlxrwMSSpZZggS1ILyMwbgBsGbFtVef524O2D7HcPcPzA7ZJ0oNp5eTwTZEmSJI1YsyW19WSCXNHOv4TGWi1tYrtJ0tjxO0yqn5oS5Ig4FbiMYu7bpzLzIwPK3wOcUznmHGBGZj4cEfcC24G9PDknrinZIYyebSdJjWU/LNXPsAlyLbc4zcy/A/6urP8G4H9m5sOVw7TMLU4lSZI0sdWyzNu+W5xm5i6g7xanQ+kE1tYjOEmSJGm81ZIg13yL04g4FDgV+EJls7c4lSRJUsuoJUGu+RanwBuAbw6YXvGKzHwRsBB4V0S8crAdM/OKzOzIzI4ZM2bUEJYkSZJUf7UkyDXd4rR0FgOmV3iLU0mSJLWSWhLkYW9xChARTwdeBfzvyjZvcSpJkqSWMuwqFjXe4hTgjcBNmbmjsru3OJUkSVJLqWkd5OFucVq+vhK4csA2b3EqSZKkllLLFAtJkiRpwjBBliRJkipMkCVJkqSKaMZ7t0fEVuC+RscxhCMBb5s9crbb6Nl2o9fMbXdMZjbtou/2w23Lths92250mr3dBu2LmzJBbmYRsTEzOxodR6ux3UbPths92649+fc6erbd6Nl2o9Oq7eYUC0mSJKnCBFmSJEmqMEEeuSsaHUCLst1Gz7YbPduuPfn3Onq23ejZdqPTku3mHGRJkiSpwhFkSZIkqcIEWZIkSapo2gQ5Im5pdAyjERGPjdFxL46Ivy6fXxkRZwxRz3brf9yB7faziPh+RNwREX8yFu95oCLi2IjoHaNj7/u3ExFfi4iWW3pnKOPcbj8u/w3dFhEnjMV7Ngv7lKcc1754dMdtqb7Yfnh02qkfbtoEOTP/qNExAETEpEbHMBK227Dek5knAH8JrGpsKGph52Tm8cAngb9rdDBjyT5ldGy3YdkX60CNaT/ctAly36/YiDg5Ir4eEZ+LiJ9ExEci4pyI+E5E/DAifr+s94aI+HZEfC8i/m9EPKfcPiMivhIR342If4qI+yLiyLLsz8vjfL8sm9T33hHxwYj4NvA3EXFNJa7XRMQXh4l9efmr5tZKHEPFd3FEdJe/hu6JiP9ROc7S8hfS/wX+o+1W13b7FnBULW06TMxviYgflHH/awwYUYrKaExEvKf8pfuDiFg2zKEnRcQ/R8SdEXFTRBxSHuMd5THuiIgvRMSh5fYrI+LjEXFL2R59v7IjIi6PiLsi4nrg2Qf6meuhjdqtLv+Ompl9in3xGLfbAf8fivbpT8ZVG7Xb2PTDmdmUD+Cx8s+TgUeA5wFPA34BLCvL3g18rHz+TJ5clePtwN+Xzy8H3l8+PxVIitsezgH+DzC5LPsk8JbyeQJvKp8H8CNgRvl6DfCG/cSdfeXApcDfDBPfxcAt5Wc7EngImAy8GPghcChwOHA38NflPlcCZ9huo2834HRgzQH+G50H/Bg4snx9xMC/m8rfxykUS90ExQ/TdcArhzjuscAe4ITy9eeAPy+fP6tS70NAV+Wz/Xt57LnA3eX2PwW+AkwCfq/8N9HXBl8DOhrwf7tt2o1i9GvFeLfhOP992afYF49Zu3GAfTFt1J+M8//rtmk3xqgfPpjWcFtm/hIgIn4K3FRu/yGwoHw+E/hsRDwPmAL8rNx+EvBGgMz8UkRsK7f/CcV/4NsiAuAQ4IGybC/whXKfjIh/Bf48Ij4NvBx4y35i3UXxjwfgduA1w8QHcH1mPg48HhEPAM8B/hi4JjN/W37u6/bbQoOz3Z7abn8XEZdS/BJ92X7iqcWrgc9n5oMAmflw2SaDOaV8fK98fRhwHHDzEPV/lpnfL5/fTtHpAMyPiA8BzyiP8eXKPtdm5hPAXX2jO8ArgbWZuRfYEhHra/50Y6cd2u0zETGNouN+0dAfte3Yp9gXN1tf3A79SSO0Q7uNaT/cKgny45XnT1ReP8GTn2El8NHMvC4iTqb4VQvFL57BBPAvmfn+Qcp2ln8hfT5N8Ut9J/DvmblnP7HuzvInDUUnNVx80P/zVfdJDozt9lTvAb4I/A/gXyi+YEYrBnmvPZRTl6LobaZU6n44M/+pxmMP/GyHlM+vBE7PzDsi4m0Uo1OD7VP9+zvQf0f11g7tdg5wB/AR4BMUIx0TgX3K6NhuT1Wvvrgd+pNGaId2G9N+uGnnII/C0ylOXQG8tbJ9A/AmgIg4heI0EcBXgTMi4tll2RERccxgB87MLcAW4G8o/oLrGd9QbgbeGBGHRMR04A2jfN/RxtW27Vb+Sr0MOCgi/sso44KiLd4UEc+Coi2Ae3myoz+N4lQjFL+UF0XEYWXdo/racISmA7+MiMkUncNwbgbOiohJ5cjPguF2GAdt0W6ZuZvi3/bLImLOKGJqVxOuT6mTCddudeqL26I/aYC2aLex7IdbZQS5FhcD/x4RvwBuBWaX25cBayPiTODrwC+B7Zn5YET8DXBTRBwE7AbeBdw3xPE/QzGH6646xzeozPxuRHwW+H4Z0zcGVPmniPhY+fz+zHx5neNq13brq5flqZ730v80T80y886IWA58PSL2Upx+WgL874j4DkUHtKOse1P5n/db5Wmsx4A/58lTorX6X8C3KT7bDyk6nP25huJU2g+Bn1D8XVZdHxG7y+ffysz/OsJ4RqxN2q3vs/wuIv4e+Gtg8QhjalcXM7H6FPviQYxXX9wm/Yn98OAa2g+3/a2mI+JpwN7M3BMRLwf+MYulZUZ6nMuB72Xm6nrH2IxsN0n1ZJ8yOrab1BjtNII8lFnA58pf2LuAd4z0ABFxO8UvqQvqHFszs90k1ZN9yujYblIDtP0I8liJYn3Jpw3Y/ObM/GEj4mkVttuTyrlfXx2k6E8y86HxjqdV2G6qsk8ZHdutYH8yOhOh3UyQJUmSpIp2WsVCkiRJOmAmyJIkSVKFCbIkSZJUYYIsSZIkVZggS5IkSRX/P/XIcHqWMflBAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "fig.suptitle('Selected Trials')\n",
    "\n",
    "axs[0].set_title('Maximum values')\n",
    "axs[0].boxplot([ good_trials[cl].max(axis = (0,1)) for cl in classes ], labels=classes)\n",
    "\n",
    "axs[1].set_title('Standard deviations')\n",
    "axs[1].boxplot([ good_trials[cl].std(axis = (0,1)) for cl in classes ], labels=classes)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "# Combine MI trials\n",
    "\n",
    "If you want to classify idle/MI this is useful"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(8, 500, 377)\n"
     ]
    }
   ],
   "source": [
    "good_trials['mi'] = np.concatenate([\n",
    "    good_trials['imagery_handL'][:,:,:],\n",
    "    good_trials['imagery_handR'][:,:,:]\n",
    "], axis=2)\n",
    "\n",
    "print(good_trials['mi'].shape)"
   ]
  },
  {
   "source": [
    "# Combine Idle Trials\n",
    "\n",
    "If you are classifying idle/left/right only take half of the idle data so that the dataset is balanced."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(8, 375, 381)\n"
     ]
    }
   ],
   "source": [
    "# good_trials['idle'] = np.concatenate([\n",
    "#     good_trials['cue_handL'][:,:,-90:],\n",
    "#     good_trials['cue_handR'][:,:,-90:]\n",
    "# ], axis=2)\n",
    "\n",
    "good_trials['idle'] = np.concatenate([\n",
    "    good_trials['cue_handL'][:,:,:],\n",
    "    good_trials['cue_handR'][:,:,:]\n",
    "], axis=2)\n",
    "\n",
    "\n",
    "print(good_trials['idle'].shape)"
   ]
  },
  {
   "source": [
    "# Analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Classes: ['imagery_handL', 'imagery_handR']\nShape of EEG: (8, 1023780)\nSample rate: 250\nNumber of channels: 6\nFrequency band for bandpower: [8, 16]\n"
     ]
    }
   ],
   "source": [
    "# ----------------------------------------------------------------\n",
    "# H-PARAMS\n",
    "# ----------------------------------------------------------------\n",
    "classes = ['imagery_handL', 'imagery_handR']\n",
    "# classes = ['imagery_handL', 'imagery_handR', 'idle']\n",
    "# classes = ['idle', 'mi']\n",
    "nfolds = 5\n",
    "nclasses = len(classes)\n",
    "band = [8, 16]\n",
    "channels = [1,3,4,5, 6, 7]\n",
    "nchannels = len(channels)\n",
    "classifier = skLDA()\n",
    "# ----------------------------------------------------------------\n",
    "\n",
    "# Print some information\n",
    "print('Classes:', classes)\n",
    "print('Shape of EEG:', EEG.shape)\n",
    "print('Sample rate:', fs)\n",
    "print('Number of channels:', nchannels)\n",
    "print('Frequency band for bandpower:', band)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "trials_bp = {}\n",
    "for cl in classes:\n",
    "    freqs, bandpower = analysis.bandpower(good_trials[cl], fs, band=band)\n",
    "    trials_bp[cl] = bandpower\n",
    "\n",
    "analysis.plot_bandpower(trials_bp, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 1.6,  3.6,  5.6,  7.6,  9.6, 11.6, 13.6])"
      ]
     },
     "metadata": {},
     "execution_count": 106
    }
   ],
   "source": [
    "np.arange(1,nchannels)*2.0-0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x360 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "nchannels = trials[classes[0]].shape[0]\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "for i, cl in enumerate(classes):\n",
    "    plt.boxplot(\n",
    "        [trials_bp[cl][ch,:] for ch in range(nchannels)],\n",
    "        positions=np.arange(0,nchannels)*nclasses-i*0.8,\n",
    "        widths=0.6,\n",
    "        patch_artist=True,\n",
    "        boxprops=dict(facecolor='b', color='orange'),\n",
    "        labels=['Fz', 'C3', 'Cz', 'C4', 'Pz', 'PO7', 'Oz', 'PO8']\n",
    "    )"
   ]
  },
  {
   "source": [
    "## LDA"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as skLDA\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find size of cross val datasets\n",
    "test_set_shapes = {}\n",
    "train_set_shapes = {}\n",
    "for cl in classes:\n",
    "    test_set_shapes[cl] = int(good_trials[cl].shape[2] / nfolds)\n",
    "    train_set_shapes[cl] = good_trials[cl].shape[2] - test_set_shapes[cl]\n",
    "\n",
    "\n",
    "# Places to plot/view the results\n",
    "accuracies = {}\n",
    "class_accuracies = {}\n",
    "confusion_matrices = {}\n",
    "\n",
    "# Run cross validation for k folds\n",
    "for i in range(nfolds):    \n",
    "    train = {}\n",
    "    test = {}\n",
    "    for cl in classes:\n",
    "        ntest = test_set_shapes[cl]\n",
    "        train[cl] = np.concatenate(\n",
    "            [good_trials[cl][:,:,:i*ntest], \n",
    "            good_trials[cl][:,:,(i+1)*ntest:]], axis=2)\n",
    "        test[cl] = good_trials[cl][:,:,i*ntest:(i+1)*ntest]\n",
    "\n",
    "    # Bandpower\n",
    "    for cl in classes:\n",
    "        _, bandpower = analysis.bandpower(train[cl], fs, band)\n",
    "        train[cl] = bandpower[channels,:]\n",
    "        _, bandpower = analysis.bandpower(test[cl], fs, band)\n",
    "        test[cl] = bandpower[channels,:]\n",
    "    \n",
    "    # Build sk-learn compatible matrices\n",
    "    X_train = np.empty([nchannels,0])\n",
    "    y_train = np.array([])\n",
    "    X_test = np.empty([nchannels,0])\n",
    "    y_test = np.array([])\n",
    "    for cl in classes:\n",
    "        X_train = np.concatenate([X_train, train[cl]], axis=1)\n",
    "        y_train = np.concatenate([y_train, np.ones(train[cl].shape[1]) * label2code[cl]], axis=0)\n",
    "        X_test = np.concatenate([X_test, test[cl]], axis=1)\n",
    "        y_test = np.concatenate([y_test, np.ones(test[cl].shape[1]) * label2code[cl]], axis=0)\n",
    "    \n",
    "    X_train =  X_train.T\n",
    "    X_test =  X_test.T\n",
    "    \n",
    "    # LDA classifier\n",
    "    classifier.fit(X_train, y_train)\n",
    "    y_pred = classifier.predict(X_test)\n",
    "\n",
    "    confusion_matrices[i] = confusion_matrix(y_test, y_pred)\n",
    "    accuracies[i] = np.sum(y_pred == y_test) / y_pred.size\n",
    "    class_accuracies[i] = {}\n",
    "    for cl in classes:\n",
    "        class_accuracies[i][cl] = np.sum(\n",
    "            np.logical_and(y_pred == y_test, y_pred == label2code[cl])) / np.sum(y_test == label2code[cl]) \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7] [8, 16]\n{'imagery_handL': 0.3783783783783784, 'imagery_handR': 0.6736842105263158}\n"
     ]
    }
   ],
   "source": [
    "av_class_accuracy = {}\n",
    "for i in range(nfolds):\n",
    "    for cl in classes:\n",
    "        if cl in av_class_accuracy:\n",
    "            av_class_accuracy[cl] += class_accuracies[i][cl]\n",
    "        else:\n",
    "            av_class_accuracy[cl] = class_accuracies[i][cl]\n",
    "\n",
    "for k,v in av_class_accuracy.items():\n",
    "    av_class_accuracy[k] = v/nfolds\n",
    "\n",
    "print(channels, band)\n",
    "print(av_class_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}