Process a big data EEG resource (TUH EEG Corpus)

In this example, we showcase usage of the Temple University Hospital EEG Corpus ( including simple preprocessing steps as well as cutting of compute windows.

# Author: Lukas Gemein <>
# License: BSD (3-clause)

import os

import numpy as np
import matplotlib.pyplot as plt'seaborn')
import mne

from braindecode.datasets import TUH
from braindecode.datautil.preprocess import preprocess, Preprocessor
from braindecode.datautil.windowers import create_fixed_length_windows
from braindecode.datautil.serialization import (
    save_concat_dataset, load_concat_dataset)

mne.set_log_level('ERROR')  # avoid messages everytime a window is extracted

We start by creating a TUH dataset. First, the class generates a description of the recordings in TUH_PATH (which is later accessible as tuh.description) without actually touching the files. This will parse information from file paths such as patient id, recording data, etc and should be really fast. Afterwards, the files are sorted chronologically by year, month, day, patient id, recording session and segment. In the following, a subset of the description corresponding to recording_ids is used. Afterwards, the files will be iterated a second time, slower than before. The files are now actually touched. Additional information about subjects like age and gender are parsed directly from the EDF file header. If existent, the physician report is added to the description. Furthermore, the recordings are read with with preload=False. Finally, we will get a BaseConcatDataset of BaseDatasets each holding a single which is fully compatible with other braindecode functionalities.

TUH_PATH = '/home/lukas/Downloads/tuh_eeg_sample/'
tuh = TUH(

We can easily create descriptive statistics using the description DataFrame, for example an age histogram split by gender of patients.

fig, ax = plt.subplots(1, 1, figsize=(15, 5))
genders = tuh.description.gender.unique()
x = [tuh.description.age[tuh.description.gender == g] for g in genders]
    bins=np.arange(100, dtype=int),

Next, we will perform some preprocessing steps. First, we will do some selection of available recordings based on the duration. We will select those recordings, that have at least five minutes duration. Data is not loaded here.

def select_by_duration(ds, tmin=0, tmax=None):
    # determine length of the recordings and select based on tmin and tmax
    duration = ds.description.n_samples / ds.description.sfreq
    duration = duration[duration >= tmin]
    if tmax is None:
        tmax = np.inf
    duration = duration[duration <= tmax]
    split_ids = list(duration.index)
    splits = ds.split(split_ids)
    split = splits['0']
    return split

tmin = 5 * 60
tmax = None
tuh = select_by_duration(tuh, tmin, tmax)

Next, we will discard all recordings that have an incomplete channel configuration (wrt the channels that we are interested in, i.e. the 21 channels of the international 10-20-placement). The dataset is subdivided into recordings with ‘le’ and ‘ar’ reference which we will have to consider. Data is not loaded here.

short_ch_names = sorted([
    'A1', 'A2',
    'FP1', 'FP2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2',
    'F7', 'F8', 'T3', 'T4', 'T5', 'T6', 'FZ', 'CZ', 'PZ'])
ar_ch_names = sorted([
    'EEG A1-REF', 'EEG A2-REF',
    'EEG FP1-REF', 'EEG FP2-REF', 'EEG F3-REF', 'EEG F4-REF', 'EEG C3-REF',
    'EEG C4-REF', 'EEG P3-REF', 'EEG P4-REF', 'EEG O1-REF', 'EEG O2-REF',
    'EEG F7-REF', 'EEG F8-REF', 'EEG T3-REF', 'EEG T4-REF', 'EEG T5-REF',
le_ch_names = sorted([
    'EEG A1-LE', 'EEG A2-LE',
    'EEG FP1-LE', 'EEG FP2-LE', 'EEG F3-LE', 'EEG F4-LE', 'EEG C3-LE',
    'EEG C4-LE', 'EEG P3-LE', 'EEG P4-LE', 'EEG O1-LE', 'EEG O2-LE',
    'EEG F7-LE', 'EEG F8-LE', 'EEG T3-LE', 'EEG T4-LE', 'EEG T5-LE',
    'EEG T6-LE', 'EEG FZ-LE', 'EEG CZ-LE', 'EEG PZ-LE'])
assert len(short_ch_names) == len(ar_ch_names) == len(le_ch_names)
ar_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(
    ar_ch_names, short_ch_names)}
le_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(
    le_ch_names, short_ch_names)}
ch_mapping = {'ar': ar_ch_mapping, 'le': le_ch_mapping}

def select_by_channels(ds, ch_mapping):
    split_ids = []
    for i, d in enumerate(ds.datasets):
        seta = set(ch_mapping[d.description.reference].keys())
        setb = set(d.raw.ch_names)
        if seta.issubset(setb):
    return ds.split(split_ids)['0']

tuh = select_by_channels(tuh, ch_mapping)

Next, we will chain several preprocessing steps that are realized through mne. Data will be loaded by the first preprocessor that has a mention of it in brackets: - crop the recordings to a region of interest - re-reference all recordings to ‘ar’ (requires load) - rename channels to short channel names - pick channels of interest - scale signals to microvolts (requires load) - resample recordings to a common frequency (requires load) - create compute windows

def custom_rename_channels(raw, mapping):
    # rename channels which are dependent on referencing:
    # le: EEG 01-LE, ar: EEG 01-REF
    # mne fails if the mapping contains channels as keys that are not present
    # in the raw
    reference = raw.ch_names[0].split('-')[-1].lower()
    assert reference in ['le', 'ref'], 'unexpected referencing'
    reference = 'le' if reference == 'le' else 'ar'

def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
    # crop recordings to tmin – tmax. can be incomplete if recording
    # has lower duration than tmax
    # by default mne fails if tmax is bigger than duration
    tmax = min((raw.n_times - 1) /['sfreq'], tmax)
    raw.crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax)

tmin = 1 * 60
tmax = 6 * 60
sfreq = 100

preprocessors = [
    Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,
    Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),
    Preprocessor(custom_rename_channels, mapping=ch_mapping,
    Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),
    Preprocessor(lambda x: x * 1e6),
    Preprocessor('resample', sfreq=sfreq),

The preprocessing loop works as follows. For every recording, we apply the preprocessors as defined above. Then, we update the description of the rec, since we have altered the duration, the reference, and the sampling frequency. Afterwards, we split the continuous signals into compute windows. We store each recording to a unique subdirectory that is named corresponding to the rec id. To save memory, after windowing and storing, we delete the raw dataset and the windows dataset, respectively.

window_size_samples = 1000
window_stride_samples = 1000
create_compute_windows = True

out_i = 0
errors = []
OUT_PATH = './tuh_sample/'
tuh_splits = tuh.split([[i] for i in range(len(tuh.datasets))])
for rec_i, tuh_subset in tuh_splits.items():
    # implement preprocess for BaseDatasets? Would remove necessity
    # to split above
    preprocess(tuh_subset, preprocessors)

    # update description of the recording(s)
    tuh_subset.description.sfreq = len(tuh_subset.datasets) * [sfreq]
    tuh_subset.description.reference = len(tuh_subset.datasets) * ['ar']
    tuh_subset.description.n_samples = [len(d) for d in tuh_subset.datasets]

    if create_compute_windows:
        # generate compute windows here and store them to disk
        tuh_windows = create_fixed_length_windows(
        # save memory by deleting raw recording
        del tuh_subset
        # store the number of windows required for loading later on
        tuh_windows.description["n_windows"] = [len(d) for d in

        # create one directory for every recording
        rec_path = os.path.join(OUT_PATH, str(rec_i))
        if not os.path.exists(rec_path):
        save_concat_dataset(rec_path, tuh_windows)
        out_i += 1
        # save memory by catching epoched recording
        del tuh_windows
        # store raws to disk for option of using different compute window
        # sizes

We load the preprocessed data again in a lazy fashion (preload=False). It is now ready to be used for model training.

tuh_loaded = load_concat_dataset('./tuh_sample/', preload=False)

Total running time of the script: ( 0 minutes 0.000 seconds)

Estimated memory usage: 0 MB

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