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MNE Dataset Example#
This example shows how to convert data from mne.Raws or mne.Epochs 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_mne_raw, create_from_mne_epochs)
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]
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R05.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R06.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R09.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R10.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R13.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R14.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Convert Raw objects to a compatible data format:
descriptions = [{"event_code": code, "subject": subject_id}
for code in event_codes]
windows_dataset = create_from_mne_raw(
parts,
trial_start_offset_samples=0,
trial_stop_offset_samples=0,
window_size_samples=500,
window_stride_samples=500,
drop_last_window=False,
descriptions=descriptions,
)
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:124: UserWarning: Drop bad windows only has an effect if mne epochs are created, and this argument may be removed in the future.
warnings.warn('Drop bad windows only has an effect if mne epochs are created, '
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:130: UserWarning: Using reject or picks or flat or dropping bad windows means mne Epochs are created, which will be substantially slower and may be deprecated in the future.
warnings.warn('Using reject or picks or flat or dropping bad windows means '
Used Annotations descriptions: ['T0', 'T1', 'T2']
Using data from preloaded Raw for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Using data from preloaded Raw for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Using data from preloaded Raw for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Using data from preloaded Raw for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Using data from preloaded Raw for 60 events and 500 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['T0', 'T1', 'T2']
Using data from preloaded Raw for 60 events and 500 original time points ...
0 bad epochs dropped
If trials were already cut beforehand and are available as mne.Epochs:
list_of_epochs = [mne.Epochs(raw, [[0, 0, 0]], tmin=0, baseline=None)
for raw in parts]
windows_dataset = create_from_mne_epochs(
list_of_epochs,
window_size_samples=50,
window_stride_samples=50,
drop_last_window=False
)
windows_dataset.description
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 2 events and 50 original time points ...
0 bad epochs dropped
Creating RawArray with float64 data, n_channels=64, n_times=81
Range : 0 ... 80 = 0.000 ... 0.500 secs
Ready.
Adding metadata with 4 columns
2 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 2 events and 50 original time points ...
0 bad epochs dropped
Total running time of the script: (0 minutes 1.321 seconds)
Estimated memory usage: 10 MB