09_eeg_use_case.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_files = glob.glob('data/eeg_use_case/train/subj7*data.csv')\n",
    "all_event_files = glob.glob('data/eeg_use_case/train/subj7*events.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "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": 39,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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