MOABB Dataset Example

In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode.

# Authors: Lukas Gemein <l.gemein@gmail.com>
#          Hubert Banville <hubert.jbanville@gmail.com>
#          Simon Brandt <simonbrandt@protonmail.com>
#          Daniel Wilson <dan.c.wil@gmail.com>
#
# License: BSD (3-clause)

from braindecode.datasets import MOABBDataset
from braindecode.preprocessing import preprocess, Preprocessor

First, we create a dataset based on BCIC IV 2a fetched with MOABB,

dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])

48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]

The dataset has a pandas DataFrame with additional description of its internal datasets

dataset.description

	subject	session	run
0	1	session_T	run_0
1	1	session_T	run_1
2	1	session_T	run_2
3	1	session_T	run_3
4	1	session_T	run_4
5	1	session_T	run_5
6	1	session_E	run_0
7	1	session_E	run_1
8	1	session_E	run_2
9	1	session_E	run_3
10	1	session_E	run_4
11	1	session_E	run_5

We can iterate through dataset which yields one time point of a continuous signal x, and a target y (which can be None if targets are not defined for the entire continuous signal).

for x, y in dataset:
    print(x.shape, y)
    break

(26, 1) None

We can apply preprocessing transforms that are defined in mne and work in-place, such as resampling, bandpass filtering, or electrode selection.

preprocessors = [
    Preprocessor('pick_types', eeg=True, meg=False, stim=True),
    Preprocessor('resample', sfreq=100)
]
print(dataset.datasets[0].raw.info["sfreq"])
preprocess(dataset, preprocessors)
print(dataset.datasets[0].raw.info["sfreq"])

250.0
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
100.0

We can easily split the dataset based on a criteria applied to the description DataFrame:

subsets = dataset.split("session")
print({subset_name: len(subset) for subset_name, subset in subsets.items()})

{'session_E': 232164, 'session_T': 232164}

See our Trialwise Decoding and Cropped Decoding examples for training with this dataset.

Estimated memory usage: 125 MB