{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EEG Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "    \n",
    "This example shows how to compute EEG frequency band power from EEG data acquired by the Muse Headband.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from fau_colors import cmaps\n",
    "import biopsykit as bp\n",
    "\n",
    "%matplotlib widget\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "palette = sns.color_palette(cmaps.faculties)\n",
    "sns.set_theme(context=\"notebook\", style=\"ticks\", font=\"sans-serif\", palette=palette)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 4)\n",
    "plt.rcParams[\"pdf.fonttype\"] = 42\n",
    "plt.rcParams[\"mathtext.default\"] = \"regular\"\n",
    "\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = bp.example_data.get_eeg_example()\n",
    "data = dataset.data_as_df(index=\"local_datetime\")\n",
    "# alternatively, load your own data:\n",
    "# dataset = bp.io.eeg.MuseDataset(\"<path-to-muse-eeg-data>\")\n",
    "# data = dataset.data_as_df(index=\"<specify-your-index-type\">)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a [EegProcessor](https://biopsykit.readthedocs.io/en/latest/api/biopsykit.signals.eeg.html#biopsykit.signals.eeg.EegProcessor) instance for processing EEG raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute Relative EEG Frequency Band Energy\n",
    "eeg_processor = bp.signals.eeg.EegProcessor(data, dataset.sampling_rate_hz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_processor.relative_band_energy()\n",
    "df_bands = eeg_processor.eeg_result[\"Data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For saving the frequency band data frame\n",
    "# bp.io.eeg.write_frequency_bands(df_bands, \"<path-to-save.csv>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "df_bands.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "# compute 20-point moving average and plot this\n",
    "df_bands.rolling(20).mean().plot(ax=ax)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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