Custom Dataset Example

This example shows how to convert data X and y as numpy arrays to a braindecode compatible data format.

# Authors: Lukas Gemein <l.gemein@gmail.com>
#
# License: BSD (3-clause)

import mne

from braindecode.datasets import create_from_X_y

To set up the example, we first fetch some data using MNE:

# 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet
subject_id = 22
event_codes = [5, 6, 9, 10, 13, 14]
# event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]

# This will download the files if you don't have them yet,
# and then return the paths to the files.
physionet_paths = mne.datasets.eegbci.load_data(
    subject_id, event_codes, update_path=False
)

# Load each of the files
parts = [
    mne.io.read_raw_edf(path, preload=True, stim_channel="auto")
    for path in physionet_paths
]

We take the required data, targets and additional information sampling frequency and channel names from the loaded data. Note that this data and information can originate from any source.

X = [raw.get_data() for raw in parts]
y = event_codes
sfreq = parts[0].info["sfreq"]
ch_names = parts[0].info["ch_names"]

Convert to data format compatible with skorch and braindecode:

windows_dataset = create_from_X_y(
    X,
    y,
    drop_last_window=False,
    sfreq=sfreq,
    ch_names=ch_names,
    window_stride_samples=500,
    window_size_samples=500,
)

windows_dataset.description  # look as dataset description

	target
0	5
1	6
2	9
3	10
4	13
5	14

You can manipulate the dataset

print(len(windows_dataset))  # get the number of samples

240

You can now index the data

i = 0
x_i, y_i, window_ind = windows_dataset[0]
n_channels, n_times = x_i.shape  # the EEG data
_, start_ind, stop_ind = window_ind
print(f"n_channels={n_channels}  -- n_times={n_times} -- y_i={y_i}")
print(f"start_ind={start_ind} -- stop_ind={stop_ind}")

n_channels=64  -- n_times=500 -- y_i=5
start_ind=0 -- stop_ind=500

Estimated memory usage: 853 MB