braindecode datasets examples

braindecode_mne_data_example.ipynb

braindecode_xy_data_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mne\n",
    "\n",
    "# 5,6,7,10,13,14 are codes for executed and imagined hands/feet\n",
    "subject_id = 22\n",
    "event_codes = [5,6,9,10,13,14]\n",
    "#event_codes = [3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "\n",
    "# This will download the files if you don't have them yet,\n",
    "# and then return the paths to the files.\n",
    "physionet_paths = mne.datasets.eegbci.load_data(subject_id, event_codes)\n",
    "\n",
    "# Load each of the files\n",
    "parts = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto', verbose='WARNING')\n",
    "         for path in physionet_paths]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use Braindecode if you have preprocessed data as X and y, where X holds your data cut to trials and y targets corresponding to the trials. In addition, you need to know the channel names of your data as well as the sampling frequency of the signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfreq = parts[0].info[\"sfreq\"]\n",
    "ch_names = parts[0].info[\"ch_names\"]\n",
    "X = [raw.get_data() for raw in parts]\n",
    "y = event_codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gemeinl/anaconda3/envs/braindecode_v2/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import mne\n",
    "import pandas as pd\n",
    "\n",
    "from braindecode.datasets.base import BaseDataset, BaseConcatDataset\n",
    "from braindecode.datautil.windowers import create_fixed_length_windows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- First, create a mne.RawArray for all x in X also using the sampling frequency and channel names for mne.Info.  \n",
    "- Second, Transform the mne.RawArrays to BaseDatasets. Enter the targets per trial into the pandas.Series object and set target_name corresponding to the column where the targets are located in the pandas.Series.  \n",
    "- Third, concatenate the BaseDatasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n"
     ]
    }
   ],
   "source": [
    "base_datasets = []\n",
    "for x, target in zip(X, y):\n",
    "    info = mne.create_info(ch_names=ch_names, sfreq=sfreq)\n",
    "    raw = mne.io.RawArray(x, info)\n",
    "    base_dataset = BaseDataset(raw, pd.Series({\"target\": target}), target_name=\"target\")\n",
    "    base_datasets.append(base_dataset)\n",
    "base_datasets = BaseConcatDataset(base_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use create_fixed_lenth_windows to do the supercrop computation for you which is needed for decoding with skorch and braindecode. Set the parameters, such that there fits a single supercrop in your pre-cut trials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n"
     ]
    }
   ],
   "source": [
    "windows_datasets = create_fixed_length_windows(\n",
    "    base_datasets,\n",
    "    start_offset_samples=0,\n",
    "    stop_offset_samples=0,\n",
    "    supercrop_size_samples=x.shape[1],\n",
    "    supercrop_stride_samples=x.shape[1],\n",
    "    drop_samples=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for 1 events and 20000 original time points ...\n",
      "(64, 20000) 5 [0, 0, 20000]\n"
     ]
    }
   ],
   "source": [
    "for x, y, ind in windows_datasets:\n",
    "    break\n",
    "print(x.shape, y, ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gemeinl/anaconda3/envs/braindecode_v2/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# TODO: add this function to braindecode\n",
    "import mne\n",
    "import pandas as pd\n",
    "\n",
    "from braindecode.datasets.base import BaseDataset, BaseConcatDataset\n",
    "from braindecode.datautil.windowers import create_fixed_length_windows\n",
    "\n",
    "def create_from_X_y(X, y, sfreq, ch_names, supercrop_size_samples=None, supercrop_stride_samples=None, drop_samples=False):\n",
    "    base_datasets = []\n",
    "    for x, target in zip(X, y):\n",
    "        info = mne.create_info(ch_names=ch_names, sfreq=sfreq)\n",
    "        raw = mne.io.RawArray(x, info)\n",
    "        base_dataset = BaseDataset(raw, pd.Series({\"target\": target}), target_name=\"target\")\n",
    "        base_datasets.append(base_dataset)\n",
    "    base_datasets = BaseConcatDataset(base_datasets)\n",
    "    \n",
    "    if supercrop_size_samples is None:\n",
    "        supercrop_size_samples = x.shape[1]\n",
    "    if supercrop_stride_samples is None:\n",
    "        supercrop_stride_samples = x.shape[1]\n",
    "    windows_datasets = create_fixed_length_windows(\n",
    "        base_datasets,\n",
    "        start_offset_samples=0,\n",
    "        stop_offset_samples=0,\n",
    "        supercrop_size_samples=supercrop_size_samples,\n",
    "        supercrop_stride_samples=supercrop_stride_samples,\n",
    "        drop_samples=drop_samples\n",
    "    )\n",
    "    return windows_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "Creating RawArray with float64 data, n_channels=64, n_times=20000\n",
      "    Range : 0 ... 19999 =      0.000 ...   124.994 secs\n",
      "Ready.\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n",
      "1 matching events found\n",
      "No baseline correction applied\n",
      "Adding metadata with 4 columns\n",
      "0 projection items activated\n",
      "Loading data for 1 events and 20000 original time points ...\n",
      "0 bad epochs dropped\n"
     ]
    }
   ],
   "source": [
    "windows_dataset = create_from_X_y(X, y, sfreq, ch_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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fix_create_from_mne_epochs.ipynb