09_eeg_use_case.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "from scipy.signal import resample\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from itertools import islice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.read_csv('../ml-use-case-eeg/train/subj10_series1_data.csv').loc[::100, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = 'data/eeg_use_case/train/'\n",
    "test_path = 'data/eeg_use_case/test/'\n",
    "all_files = os.listdir(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_files = glob.glob('data/eeg_use_case/train/*data.csv')\n",
    "all_event_files = glob.glob('data/eeg_use_case/train/*events.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(file_names):\n",
    "    # read the csv file and drop the id column\n",
    "    dfs = []\n",
    "    for f in file_names:\n",
    "        df = pd.read_csv(f)\n",
    "        df = df.drop('id', axis = 1)\n",
    "        dfs.append(df)\n",
    "    #all_dfs = pd.concat(dfs)\n",
    "    all_dfs = dfs\n",
    "    return all_dfs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = np.concatenate(load_data(all_data_files))[:,1:]\n",
    "all_events = np.concatenate(load_data(all_event_files))[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sliding_window(seq, n):\n",
    "#returns an iterator for a sliding window\n",
    "    it = iter(seq)\n",
    "    result = tuple(islice(it, n))\n",
    "    if len(result) == n:\n",
    "        yield result\n",
    "    for elem in it:\n",
    "        result = result[1:] + (elem,)\n",
    "        yield result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "win_size = 500\n",
    "iter_window = sliding_window(all_data, win_size)\n",
    "\n",
    "all_mean_power = []\n",
    "\n",
    "for window in iter_window:\n",
    "    window = np.vstack(window)\n",
    "    all_mean_power.append(np.mean(window**2, axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 452366.678,  910243.046,  430559.552,  149318.27 ,    6119.372,\n",
       "         80851.37 , 2663828.942,  124835.9  ,   15269.73 ,    6140.226,\n",
       "         28285.898,   26204.874,   87730.286,    8506.566,   60342.57 ,\n",
       "         51062.31 ,   35993.902,   33631.49 ,   16469.498,  650261.852,\n",
       "         26657.906,   68666.258,   92072.606,   24539.54 ,   96328.972,\n",
       "         36662.188,  129633.254,   40511.972,   42207.442,   27939.102,\n",
       "         36481.492,   57098.198])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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