pattern.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tempos dos vídeos\n",
    "- (IT) Insulina Tradicional (15:00) $\\rightarrow$ 900 segundos\n",
    "- (GT) Glucagon Tradicional (14:57) $\\rightarrow$ 897 segundos\n",
    "- (IA) Insulina Ativa (19:26) $\\rightarrow$ 1166 segundos\n",
    "- (GA) Glucagon Ativa (20:43) $\\rightarrow$ 1243 segundos\n",
    "\n",
    "### Participantes\n",
    "- André: IT e GA\n",
    "- Daniella: IT e GA\n",
    "- Emily: IT e GA\n",
    "- Guilherme: IT e GA\n",
    "- Murilo: GT e IA\n",
    "- Nathalia: GT e IA\n",
    "- Victoria: GT e IA\n",
    "- Yasmim: GT e IA\n",
    "- Izabel: N/A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports de bibliotecas\n",
    "\n",
    "import numpy as np\n",
    "import mne\n",
    "from scipy.signal import welch\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
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       "\n",
       "\n",
       "\n",
       "<table class=\"repr table table-hover table-striped table-sm table-responsive small\">\n",
       "    \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<tr class=\"repr-section-header general-621300e8-fae3-4613-b988-7e1fa42b3061\"  title=\"Hide section\" \n",
       "    onclick=\"toggleVisibility('general-621300e8-fae3-4613-b988-7e1fa42b3061')\">\n",
       "    <th class=\"repr-section-toggle-col\">\n",
       "        <button>\n",
       "            \n",
       "            <span class=\"collapse-uncollapse-caret\"></span>\n",
       "        </button>\n",
       "    </th>\n",
       "    <th colspan=\"2\">\n",
       "        <strong>General</strong>\n",
       "    </th>\n",
       "</tr>\n",
       "\n",
       "<tr class=\"repr-element general-621300e8-fae3-4613-b988-7e1fa42b3061 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>MNE object type</td>\n",
       "    <td>Info</td>\n",
       "</tr>\n",
       "<tr class=\"repr-element general-621300e8-fae3-4613-b988-7e1fa42b3061 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Measurement date</td>\n",
       "    \n",
       "    <td>Unknown</td>\n",
       "    \n",
       "</tr>\n",
       "<tr class=\"repr-element general-621300e8-fae3-4613-b988-7e1fa42b3061 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Participant</td>\n",
       "    \n",
       "    <td>Unknown</td>\n",
       "    \n",
       "</tr>\n",
       "<tr class=\"repr-element general-621300e8-fae3-4613-b988-7e1fa42b3061 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Experimenter</td>\n",
       "    \n",
       "    <td>Unknown</td>\n",
       "    \n",
       "</tr>\n",
       "    \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
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       "    title=\"Hide section\"  onclick=\"toggleVisibility('acquisition-864be9f3-a5e5-4131-a289-f84c6cc5fe30')\">\n",
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       "        <button>\n",
       "            \n",
       "            <span class=\"collapse-uncollapse-caret\"></span>\n",
       "        </button>\n",
       "    </th>\n",
       "    <th colspan=\"2\">\n",
       "        <strong>Acquisition</strong>\n",
       "    </th>\n",
       "</tr>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<tr class=\"repr-element acquisition-864be9f3-a5e5-4131-a289-f84c6cc5fe30 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Sampling frequency</td>\n",
       "    <td>250.00 Hz</td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<tr class=\"repr-section-header channels-c606c2be-78ae-4794-ac14-04821e9e91f7\"  title=\"Hide section\" \n",
       "    onclick=\"toggleVisibility('channels-c606c2be-78ae-4794-ac14-04821e9e91f7')\">\n",
       "    <th class=\"repr-section-toggle-col\">\n",
       "        <button>\n",
       "            \n",
       "            <span class=\"collapse-uncollapse-caret\"></span>\n",
       "        </button>\n",
       "    </th>\n",
       "    <th colspan=\"2\">\n",
       "        <strong>Channels</strong>\n",
       "    </th>\n",
       "</tr>\n",
       "\n",
       "\n",
       "<tr class=\"repr-element channels-c606c2be-78ae-4794-ac14-04821e9e91f7 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>EEG</td>\n",
       "    <td>\n",
       "        <button class=\"channel-names-btn\" onclick=\"alert('Good EEG:\\n\\nF3, Fz, F4, C3, Cz, C4, P3, P4')\" title=\"(Click to open in popup)&#13;&#13;F3, Fz, F4, C3, Cz, C4, P3, P4\">\n",
       "            8\n",
       "        </button>\n",
       "\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "<tr class=\"repr-element channels-c606c2be-78ae-4794-ac14-04821e9e91f7 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Head & sensor digitization</td>\n",
       "    \n",
       "    <td>11 points</td>\n",
       "    \n",
       "</tr>\n",
       "    \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<tr class=\"repr-section-header filters-1edaf240-0323-4604-9dde-2080638fa2fa\"  title=\"Hide section\" \n",
       "    onclick=\"toggleVisibility('filters-1edaf240-0323-4604-9dde-2080638fa2fa')\">\n",
       "    <th class=\"repr-section-toggle-col\">\n",
       "        <button>\n",
       "            \n",
       "            <span class=\"collapse-uncollapse-caret\"></span>\n",
       "        </button>\n",
       "    </th>\n",
       "    <th colspan=\"2\">\n",
       "        <strong>Filters</strong>\n",
       "    </th>\n",
       "</tr>\n",
       "\n",
       "<tr class=\"repr-element filters-1edaf240-0323-4604-9dde-2080638fa2fa \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Highpass</td>\n",
       "    <td>0.00 Hz</td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "<tr class=\"repr-element filters-1edaf240-0323-4604-9dde-2080638fa2fa \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Lowpass</td>\n",
       "    <td>125.00 Hz</td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "</table>"
      ],
      "text/plain": [
       "<Info | 8 non-empty values\n",
       " bads: []\n",
       " ch_names: F3, Fz, F4, C3, Cz, C4, P3, P4\n",
       " chs: 8 EEG\n",
       " custom_ref_applied: False\n",
       " dig: 11 items (3 Cardinal, 8 EEG)\n",
       " highpass: 0.0 Hz\n",
       " lowpass: 125.0 Hz\n",
       " meas_date: unspecified\n",
       " nchan: 8\n",
       " projs: []\n",
       " sfreq: 250.0 Hz\n",
       ">"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# definição do info (MNE)\n",
    "\n",
    "n_channels = 8\n",
    "ch_types = ['eeg'] * n_channels\n",
    "sfreq = 250\n",
    "ch_names = [\"F3\", \"Fz\", \"F4\", \"C3\", \"Cz\", \"C4\", \"P3\", \"P4\"]\n",
    "info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)\n",
    "info.set_montage(\"standard_1020\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtrando e salvando no objeto MNE cada participante\n",
    "\n",
    "def filter_and_save(tp1, tp1_test, tp2, tp2_test, test, a1, a2, subject):\n",
    "    # organização dos dados no objeto MNE\n",
    "    X = {\n",
    "        f'{a1}': mne.io.RawArray(tp1.T, info, verbose=False),\n",
    "        f'{a1}_test': mne.io.RawArray(tp1_test.T, info, verbose=False),\n",
    "        f'{a2}': mne.io.RawArray(tp2.T, info, verbose=False),\n",
    "        f'{a2}_test': mne.io.RawArray(tp2_test.T, info, verbose=False),\n",
    "        'test': mne.io.RawArray(test.T, info, verbose=False)\n",
    "    }\n",
    "    # filtragem de todos os dados e salvamento\n",
    "    for key in X:\n",
    "        X[key].notch_filter(freqs=60, verbose=False)\n",
    "        X[key].filter(l_freq=4, h_freq=100, verbose=False)\n",
    "        X[key].save(f'dataset/{subject}/' + key + '_raw.fif', overwrite=True, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento e organização dos dados de: André\n",
    "\n",
    "# aula -> 15000 até 325750 (ga) [1243 segundos]\n",
    "# teste -> 330000 até 370000 [160 segundos]\n",
    "txt = 'dataset/André/GA/OpenBCI-RAW-2023-10-25_17-43-36.txt'\n",
    "ga = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_ga = ga[15000:325751,:]\n",
    "data_ga_test = ga[330000:370001,:]\n",
    "\n",
    "# aula -> 1250 até 226250 [900 segundos]\n",
    "# teste -> 500 até 45000 [178 segundos]\n",
    "txt = 'dataset/André/IT/aula.txt'\n",
    "data_it = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))[1250:226251,:]\n",
    "txt = 'dataset/André/IT/teste.txt'\n",
    "data_it_test = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))[500:45001,:]\n",
    "\n",
    "# 500 até 149250 [595 segundos]\n",
    "txt = 'dataset/André/teste/final.txt'\n",
    "data_test = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))[500:149251,:]\n",
    "\n",
    "filter_and_save(data_ga, data_ga_test, data_it, data_it_test, data_test, 'ga', 'it', 'André')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento e organização dos dados de: Daniella\n",
    "\n",
    "# aula -> 99750 até 296500 (ga1) | 5250 até 119250 (ga2) [1243 segundos]\n",
    "# teste -> 126750 até 158000 (ga2) [125 segundos]\n",
    "ga = ['dataset/Daniella/GA/OpenBCI-RAW-2023-10-26_12-43-47.txt',\n",
    "      'dataset/Daniella/GA/OpenBCI-RAW-2023-10-26_13-06-25.txt']\n",
    "ga_ob = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in ga]\n",
    "data_ga = np.concatenate((ga_ob[0][99750:296501,:], ga_ob[1][5250:119250,:]))\n",
    "data_ga_test = ga_ob[1][126750:158001,:]\n",
    "\n",
    "# aula -> 48250 até 162000 (it1) | 500 até 111750 (it2) [900 segundos]\n",
    "# teste -> 119250 até 162000 (it2) [171 segundos]\n",
    "it = ['dataset/Daniella/IT/OpenBCI-RAW-2023-10-05_13-28-15.txt',\n",
    "      'dataset/Daniella/IT/OpenBCI-RAW-2023-10-05_13-44-46.txt']\n",
    "it_ob = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in it]\n",
    "data_it = np.concatenate((it_ob[0][48250:162001,:], it_ob[1][500:111750,:]))\n",
    "data_it_test = it_ob[1][119250:162001,:]\n",
    "\n",
    "# 26250 até 152250 [504 segundos]\n",
    "test = 'dataset/Daniella/test/OpenBCI-RAW-2023-11-28_15-22-35.txt'\n",
    "test_ob = np.loadtxt(test, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_test = test_ob[26250:152251,:]\n",
    "\n",
    "filter_and_save(data_ga, data_ga_test, data_it, data_it_test, data_test, 'ga', 'it', 'Daniella')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento e organização dos dados de: Emily\n",
    "\n",
    "# aula -> 10500 até 284750 (1) [1097] | 500 até 22750 (2) [89] [1186 segundos]\n",
    "# teste -> 500 até 50000 [198 segundos]\n",
    "txts = ['dataset/Emily/GA/1.txt', 'dataset/Emily/GA/2.txt', 'dataset/Emily/GA/3.txt']\n",
    "ga = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in txts]\n",
    "data_ga = np.concatenate((ga[0][10500:284751,:], ga[1][500:22751,:]))\n",
    "data_ga_test = ga[2][500:50001,:]\n",
    "\n",
    "# aula -> 1500 até 226500 [900 segundos]\n",
    "# teste -> 226500 até 303250 [307 segundos]\n",
    "txt = 'dataset/Emily/IT/5.txt'\n",
    "it = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_it = it[1500:226501,:]\n",
    "data_it_test = it[226500:303251,:]\n",
    "\n",
    "# 15000 até 137250 [489 segundos]\n",
    "txt = 'dataset/Emily/teste/final.txt'\n",
    "data_test = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))[15000:137251,:]\n",
    "\n",
    "filter_and_save(data_ga, data_ga_test, data_it, data_it_test, data_test, 'ga', 'it', 'Emily')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento e organização dos dados de: Guilherme\n",
    "\n",
    "# aula -> 500 até 311250 [1243]\n",
    "# teste -> 311251 até 386251 [300 segundos]\n",
    "txt = 'dataset/Guilherme/GA/1.txt'\n",
    "ga = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_ga = ga[500:311251,:]\n",
    "data_ga_test = ga[311251:386252,:]\n",
    "\n",
    "# aula -> 5000 até 225000 [900 segundos]\n",
    "# teste -> 225001 até 300001 [300 segundos]\n",
    "txt = 'dataset/Guilherme/IT/1.txt'\n",
    "it = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_it = it[5000:225001,:]\n",
    "data_it_test = it[225001:300002,:]\n",
    "\n",
    "# 1250 até 97500 [385] | 500 até 22500 [88] | 11250 até 78750 [270] => [743 segundos]\n",
    "txts = ['dataset/Guilherme/teste/1.txt', 'dataset/Guilherme/teste/3.txt', 'dataset/Guilherme/teste/4.txt']\n",
    "tests = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in txts]\n",
    "data_test = np.concatenate((tests[0][1250:97501,:], tests[1][500:22501,:], tests[2][11250:78751,:]))\n",
    "\n",
    "\n",
    "filter_and_save(data_ga, data_ga_test, data_it, data_it_test, data_test, 'ga', 'it', 'Guilherme')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento e organização dos dados de: Murilo\n",
    "\n",
    "# aula -> 500 até 224750 [897 segundos]\n",
    "# teste -> 245750 até 297750 [208 segundos]\n",
    "txt = 'dataset/Murilo/GT/1.txt'\n",
    "gt = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_gt = gt[500:224751,:]\n",
    "data_gt_test = gt[245750:297751,:]\n",
    "\n",
    "# aula -> 750 até 292250 [ segundos]\n",
    "# teste -> 351250 até 408250 [228 segundos]\n",
    "txt = 'dataset/Murilo/IA/1.txt'\n",
    "ia = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_ia = ia[750:292251,:]\n",
    "data_ia_test = ia[351250:408251,:]\n",
    "\n",
    "# 42250 até 211750 [678 segundos]\n",
    "txt = 'dataset/Murilo/teste/1.txt'\n",
    "data_test = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))[42250:211751,:]\n",
    "\n",
    "filter_and_save(data_gt, data_gt_test, data_ia, data_ia_test, data_test, 'gt', 'ia', 'Murilo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento e organização dos dados de: Nathalia\n",
    "\n",
    "# aula -> 1000 até 225250 [897 segundos]\n",
    "# teste -> 233000 até 272000 [ segundos]\n",
    "txt = 'dataset/Nathalia/GT/1.txt'\n",
    "gt = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_gt = gt[1000:225251,:]\n",
    "data_gt_test = gt[233000:272001,:]\n",
    "\n",
    "# aula -> 500 até 292000 [1166 segundos]\n",
    "# teste -> 750 até 46750 [184 segundos]\n",
    "txts = ['dataset/Nathalia/IA/2.txt', 'dataset/Nathalia/IA/3.txt']\n",
    "ia = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in txts]\n",
    "data_ia = ia[0][500:292001,:]\n",
    "data_ia_test = ia[1][750:46751,:]\n",
    "\n",
    "# 500 até 15500 [60] | 500 até 121500 [484] => [544 segundos]\n",
    "txts = ['dataset/Nathalia/teste/1.txt', 'dataset/Nathalia/teste/2.txt']\n",
    "tests = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in txts]\n",
    "data_test = np.concatenate((tests[0][500:15501,:], tests[1][500:121501,:]))\n",
    "\n",
    "filter_and_save(data_gt, data_gt_test, data_ia, data_ia_test, data_test, 'gt', 'ia', 'Nathalia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento e organização dos dados de: Victoria\n",
    "\n",
    "# aula -> 500 até 224750 [897 segundos]\n",
    "# teste -> 225000 até 260000 [140 segundos]\n",
    "txt = 'dataset/Victoria/GT/1.txt'\n",
    "gt = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_gt = gt[500:224751,:]\n",
    "data_gt_test = gt[225000:260001,:]\n",
    "\n",
    "# aula -> 500 até 292000 [1166 segundos]\n",
    "# teste -> 292500 até 347250 [219 segundos]\n",
    "txt = 'dataset/Victoria/IA/1.txt'\n",
    "ia = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))\n",
    "data_ia = ia[500:292001,:]\n",
    "data_ia_test = ia[292500:347251,:]\n",
    "\n",
    "# 67000 até 186500 [478 segundos]\n",
    "txt = 'dataset/Victoria/teste/1.txt'\n",
    "data_test = np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9))[67000:186501,:]\n",
    "\n",
    "filter_and_save(data_gt, data_gt_test, data_ia, data_ia_test, data_test, 'gt', 'ia', 'Victoria')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamento e organização dos dados de: Yasmin\n",
    "\n",
    "# aula -> 500 até 224750 [897 segundos]\n",
    "# teste -> 500 até 48000 [190 segundos]\n",
    "txts = ['dataset/Yasmin/GT/1.txt', 'dataset/Yasmin/GT/2.txt']\n",
    "gt = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in txts]\n",
    "data_gt = gt[0][500:224751,:]\n",
    "data_gt_test = gt[1][500:48001,:]\n",
    "\n",
    "# aula -> 24500 até 155500 [524] | 500 até 161000 [642] => [1166 segundos]\n",
    "# teste -> 165000 até 241250 [305 segundos]\n",
    "txts = ['dataset/Yasmin/IA/1.txt', 'dataset/Yasmin/IA/3.txt']\n",
    "ia = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in txts]\n",
    "data_ia = np.concatenate((ia[0][24500:155500,:], ia[1][500:161001,:]))\n",
    "data_ia_test = ia[1][165000:241251,:]\n",
    "\n",
    "# 17500 até 42000 [98] | 500 até 18500 [72] | 13000 até 108750 [383] => [553 segundos]\n",
    "txts = ['dataset/Yasmin/teste/1.txt', 'dataset/Yasmin/teste/2.txt', 'dataset/Yasmin/teste/3.txt']\n",
    "tests = [np.loadtxt(txt, delimiter=',', skiprows=5, usecols=range(1, 9)) for txt in txts]\n",
    "data_test = np.concatenate((tests[0][17500:42001,:], tests[1][500:18501,:], tests[2][13000:108751,:]))\n",
    "\n",
    "filter_and_save(data_gt, data_gt_test, data_ia, data_ia_test, data_test, 'gt', 'ia', 'Yasmin')"
   ]
  }
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