Moabb Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>\n",
    "#\n",
    "# License: BSD (3-clause)\n",
    "\n",
    "import moabb\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "from mne.decoding import CSP\n",
    "\n",
    "from pyriemann.estimation import Covariances\n",
    "from pyriemann.tangentspace import TangentSpace\n",
    "\n",
    "from moabb.datasets import BNCI2014002\n",
    "from moabb.paradigms import MotorImagery\n",
    "from moabb.evaluations import CrossSubjectEvaluation, WithinSessionEvaluation\n",
    "from moabb.analysis.plotting import paired_plot\n",
    "\n",
    "moabb.set_log_level('info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelines = {}\n",
    "\n",
    "pipelines['CSP + LDA'] = make_pipeline(CSP(n_components=8),\n",
    "                                       LDA())\n",
    "\n",
    "pipelines['CSP + RF'] = make_pipeline(CSP(n_components=8),\n",
    "                                     RandomForestClassifier(n_estimators=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-04-04 13:00:23 DESKTOP-V8QLV56 root[2184] WARNING Choosing from all possible events\n",
      "2020-04-04 13:00:23 DESKTOP-V8QLV56 root[2184] INFO Processing dataset: 002-2014\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      score      time  samples subject    session  channels  n_sessions  \\\n",
      "0  0.748828  1.336217    160.0       1  session_0        15           1   \n",
      "1  0.890625  1.340821    160.0       2  session_0        15           1   \n",
      "2  1.000000  1.309993    160.0       3  session_0        15           1   \n",
      "3  0.933594  1.305989    160.0       4  session_0        15           1   \n",
      "4  0.794141  1.322604    160.0       5  session_0        15           1   \n",
      "\n",
      "    dataset  pipeline  \n",
      "0  002-2014  CSP + RF  \n",
      "1  002-2014  CSP + RF  \n",
      "2  002-2014  CSP + RF  \n",
      "3  002-2014  CSP + RF  \n",
      "4  002-2014  CSP + RF  \n"
     ]
    }
   ],
   "source": [
    "paradigm = MotorImagery()\n",
    "# Because this is being auto-generated we only use 2 subjects\n",
    "dataset = BNCI2014002()\n",
    "dataset.subject_list = dataset.subject_list[:13]\n",
    "datasets = [dataset]\n",
    "overwrite = False  # set to True if we want to overwrite cached results\n",
    "evaluation = WithinSessionEvaluation(paradigm=paradigm, datasets=datasets,\n",
    "                                    suffix='examples', overwrite=overwrite)\n",
    "\n",
    "results = evaluation.process(pipelines)\n",
    "\n",
    "print(results.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       score      time  samples subject    session  channels  n_sessions  \\\n",
      "0   0.748828  1.336217    160.0       1  session_0        15           1   \n",
      "1   0.890625  1.340821    160.0       2  session_0        15           1   \n",
      "2   1.000000  1.309993    160.0       3  session_0        15           1   \n",
      "3   0.933594  1.305989    160.0       4  session_0        15           1   \n",
      "4   0.794141  1.322604    160.0       5  session_0        15           1   \n",
      "5   0.772266  1.342422    160.0       6  session_0        15           1   \n",
      "6   0.956250  1.316199    160.0       7  session_0        15           1   \n",
      "7   0.956250  1.307390    160.0       8  session_0        15           1   \n",
      "8   0.961719  1.314597    160.0       9  session_0        15           1   \n",
      "9   0.687891  1.344424    160.0      10  session_0        15           1   \n",
      "10  0.899219  1.325407    160.0      11  session_0        15           1   \n",
      "11  0.814062  1.329410    160.0      12  session_0        15           1   \n",
      "12  0.614453  1.338819    160.0      13  session_0        15           1   \n",
      "13  0.782812  0.700037    160.0       1  session_0        15           1   \n",
      "14  0.900000  0.697836    160.0       2  session_0        15           1   \n",
      "15  1.000000  0.677617    160.0       3  session_0        15           1   \n",
      "16  0.940625  0.686625    160.0       4  session_0        15           1   \n",
      "17  0.839063  0.673013    160.0       5  session_0        15           1   \n",
      "18  0.781250  0.678218    160.0       6  session_0        15           1   \n",
      "19  0.954687  0.678017    160.0       7  session_0        15           1   \n",
      "20  0.980469  0.690228    160.0       8  session_0        15           1   \n",
      "21  0.937500  0.670811    160.0       9  session_0        15           1   \n",
      "22  0.676562  0.673413    160.0      10  session_0        15           1   \n",
      "23  0.876562  0.678218    160.0      11  session_0        15           1   \n",
      "24  0.837500  0.682621    160.0      12  session_0        15           1   \n",
      "25  0.680469  0.681420    160.0      13  session_0        15           1   \n",
      "\n",
      "     dataset   pipeline  \n",
      "0   002-2014   CSP + RF  \n",
      "1   002-2014   CSP + RF  \n",
      "2   002-2014   CSP + RF  \n",
      "3   002-2014   CSP + RF  \n",
      "4   002-2014   CSP + RF  \n",
      "5   002-2014   CSP + RF  \n",
      "6   002-2014   CSP + RF  \n",
      "7   002-2014   CSP + RF  \n",
      "8   002-2014   CSP + RF  \n",
      "9   002-2014   CSP + RF  \n",
      "10  002-2014   CSP + RF  \n",
      "11  002-2014   CSP + RF  \n",
      "12  002-2014   CSP + RF  \n",
      "13  002-2014  CSP + LDA  \n",
      "14  002-2014  CSP + LDA  \n",
      "15  002-2014  CSP + LDA  \n",
      "16  002-2014  CSP + LDA  \n",
      "17  002-2014  CSP + LDA  \n",
      "18  002-2014  CSP + LDA  \n",
      "19  002-2014  CSP + LDA  \n",
      "20  002-2014  CSP + LDA  \n",
      "21  002-2014  CSP + LDA  \n",
      "22  002-2014  CSP + LDA  \n",
      "23  002-2014  CSP + LDA  \n",
      "24  002-2014  CSP + LDA  \n",
      "25  002-2014  CSP + LDA  \n"
     ]
    }
   ],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=[8, 4], sharey=True)\n",
    "\n",
    "sns.stripplot(data=results, y='score', x='pipeline', ax=axes[0], jitter=True,\n",
    "              alpha=.5, zorder=1, palette=\"Set1\")\n",
    "sns.pointplot(data=results, y='score', x='pipeline', ax=axes[0],\n",
    "              zorder=1, palette=\"Set1\")\n",
    "\n",
    "axes[0].set_ylabel('ROC AUC')\n",
    "axes[0].set_ylim(0.5, 1)\n",
    "\n",
    "# paired plot\n",
    "paired = results.pivot_table(values='score', columns='pipeline',\n",
    "                             index=['subject', 'session'])\n",
    "paired = paired.reset_index()\n",
    "\n",
    "sns.regplot(data=paired, y='CSP + RF', x='CSP + LDA', ax=axes[1],\n",
    "            fit_reg=False)\n",
    "axes[1].plot([0, 1], [0, 1], ls='--', c='k')\n",
    "axes[1].set_xlim(0.5, 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (<ipython-input-15-cc1e72c1419a>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-15-cc1e72c1419a>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    plotting.paired_plot(data=results, alg1, alg2)\u001b[0m\n\u001b[1;37m                                      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "plotting.paired_plot(data=results, alg1, alg2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>time</th>\n",
       "      <th>samples</th>\n",
       "      <th>subject</th>\n",
       "      <th>session</th>\n",
       "      <th>channels</th>\n",
       "      <th>n_sessions</th>\n",
       "      <th>dataset</th>\n",
       "      <th>pipeline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.748828</td>\n",
       "      <td>1.336217</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.890625</td>\n",
       "      <td>1.340821</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.309993</td>\n",
       "      <td>160.0</td>\n",
       "      <td>3</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.933594</td>\n",
       "      <td>1.305989</td>\n",
       "      <td>160.0</td>\n",
       "      <td>4</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.794141</td>\n",
       "      <td>1.322604</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.772266</td>\n",
       "      <td>1.342422</td>\n",
       "      <td>160.0</td>\n",
       "      <td>6</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.956250</td>\n",
       "      <td>1.316199</td>\n",
       "      <td>160.0</td>\n",
       "      <td>7</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.956250</td>\n",
       "      <td>1.307390</td>\n",
       "      <td>160.0</td>\n",
       "      <td>8</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.961719</td>\n",
       "      <td>1.314597</td>\n",
       "      <td>160.0</td>\n",
       "      <td>9</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.687891</td>\n",
       "      <td>1.344424</td>\n",
       "      <td>160.0</td>\n",
       "      <td>10</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.899219</td>\n",
       "      <td>1.325407</td>\n",
       "      <td>160.0</td>\n",
       "      <td>11</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.814062</td>\n",
       "      <td>1.329410</td>\n",
       "      <td>160.0</td>\n",
       "      <td>12</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.614453</td>\n",
       "      <td>1.338819</td>\n",
       "      <td>160.0</td>\n",
       "      <td>13</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.782812</td>\n",
       "      <td>0.700037</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.697836</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.677617</td>\n",
       "      <td>160.0</td>\n",
       "      <td>3</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.940625</td>\n",
       "      <td>0.686625</td>\n",
       "      <td>160.0</td>\n",
       "      <td>4</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.839063</td>\n",
       "      <td>0.673013</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.678218</td>\n",
       "      <td>160.0</td>\n",
       "      <td>6</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.954687</td>\n",
       "      <td>0.678017</td>\n",
       "      <td>160.0</td>\n",
       "      <td>7</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.980469</td>\n",
       "      <td>0.690228</td>\n",
       "      <td>160.0</td>\n",
       "      <td>8</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.670811</td>\n",
       "      <td>160.0</td>\n",
       "      <td>9</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.676562</td>\n",
       "      <td>0.673413</td>\n",
       "      <td>160.0</td>\n",
       "      <td>10</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.876562</td>\n",
       "      <td>0.678218</td>\n",
       "      <td>160.0</td>\n",
       "      <td>11</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.837500</td>\n",
       "      <td>0.682621</td>\n",
       "      <td>160.0</td>\n",
       "      <td>12</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.680469</td>\n",
       "      <td>0.681420</td>\n",
       "      <td>160.0</td>\n",
       "      <td>13</td>\n",
       "      <td>session_0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>002-2014</td>\n",
       "      <td>CSP + LDA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       score      time  samples subject    session  channels  n_sessions  \\\n",
       "0   0.748828  1.336217    160.0       1  session_0        15           1   \n",
       "1   0.890625  1.340821    160.0       2  session_0        15           1   \n",
       "2   1.000000  1.309993    160.0       3  session_0        15           1   \n",
       "3   0.933594  1.305989    160.0       4  session_0        15           1   \n",
       "4   0.794141  1.322604    160.0       5  session_0        15           1   \n",
       "5   0.772266  1.342422    160.0       6  session_0        15           1   \n",
       "6   0.956250  1.316199    160.0       7  session_0        15           1   \n",
       "7   0.956250  1.307390    160.0       8  session_0        15           1   \n",
       "8   0.961719  1.314597    160.0       9  session_0        15           1   \n",
       "9   0.687891  1.344424    160.0      10  session_0        15           1   \n",
       "10  0.899219  1.325407    160.0      11  session_0        15           1   \n",
       "11  0.814062  1.329410    160.0      12  session_0        15           1   \n",
       "12  0.614453  1.338819    160.0      13  session_0        15           1   \n",
       "13  0.782812  0.700037    160.0       1  session_0        15           1   \n",
       "14  0.900000  0.697836    160.0       2  session_0        15           1   \n",
       "15  1.000000  0.677617    160.0       3  session_0        15           1   \n",
       "16  0.940625  0.686625    160.0       4  session_0        15           1   \n",
       "17  0.839063  0.673013    160.0       5  session_0        15           1   \n",
       "18  0.781250  0.678218    160.0       6  session_0        15           1   \n",
       "19  0.954687  0.678017    160.0       7  session_0        15           1   \n",
       "20  0.980469  0.690228    160.0       8  session_0        15           1   \n",
       "21  0.937500  0.670811    160.0       9  session_0        15           1   \n",
       "22  0.676562  0.673413    160.0      10  session_0        15           1   \n",
       "23  0.876562  0.678218    160.0      11  session_0        15           1   \n",
       "24  0.837500  0.682621    160.0      12  session_0        15           1   \n",
       "25  0.680469  0.681420    160.0      13  session_0        15           1   \n",
       "\n",
       "     dataset   pipeline  \n",
       "0   002-2014   CSP + RF  \n",
       "1   002-2014   CSP + RF  \n",
       "2   002-2014   CSP + RF  \n",
       "3   002-2014   CSP + RF  \n",
       "4   002-2014   CSP + RF  \n",
       "5   002-2014   CSP + RF  \n",
       "6   002-2014   CSP + RF  \n",
       "7   002-2014   CSP + RF  \n",
       "8   002-2014   CSP + RF  \n",
       "9   002-2014   CSP + RF  \n",
       "10  002-2014   CSP + RF  \n",
       "11  002-2014   CSP + RF  \n",
       "12  002-2014   CSP + RF  \n",
       "13  002-2014  CSP + LDA  \n",
       "14  002-2014  CSP + LDA  \n",
       "15  002-2014  CSP + LDA  \n",
       "16  002-2014  CSP + LDA  \n",
       "17  002-2014  CSP + LDA  \n",
       "18  002-2014  CSP + LDA  \n",
       "19  002-2014  CSP + LDA  \n",
       "20  002-2014  CSP + LDA  \n",
       "21  002-2014  CSP + LDA  \n",
       "22  002-2014  CSP + LDA  \n",
       "23  002-2014  CSP + LDA  \n",
       "24  002-2014  CSP + LDA  \n",
       "25  002-2014  CSP + LDA  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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