moabb.paradigms.LeftRightImagery#

class moabb.paradigms.LeftRightImagery(fmin=8, fmax=32, events=None, tmin=0.0, tmax=None, baseline=None, channels=None, resample=None, scorer=None, overlap=None, reject_by_annotation=True)[source]#

Motor Imagery for left hand/right hand classification.

Metric is ‘roc_auc’ by default

Parameters:

fmin (float (default 8)) – cutoff frequency (Hz) for the high pass filter.
fmax (float (default 32)) – cutoff frequency (Hz) for the low pass filter.

Notes

Why 8-32 Hz by default. The default band-pass filter (fmin=8, fmax=32) keeps the mu (about 8-13 Hz) and beta (about 13-30 Hz) rhythms, whose event-related desynchronization and synchronization carry most of the motor-imagery information. It also removes slow drifts and most ocular activity, and limits the high-frequency muscle activity that a classifier could otherwise exploit instead of brain activity [3]. MOABB applies the same preprocessing to every dataset so that pipelines are compared on equal terms: the MOABB benchmark [1] filters all motor-imagery datasets to 8-32 Hz, following the BCI literature [2]. The filter is an IIR filter with MNE’s default parameters (4th-order Butterworth, applied forward and backward).

The default serves comparability; it is not a requirement. If your pipeline needs another band (deep-learning models often use a wider one), change it and report the band you used:

LeftRightImagery(fmin=4, fmax=40) filters to another band.
LeftRightImagery(fmin=None, fmax=None) disables the filter.
fmin=None alone gives a low-pass filter at fmax, and fmax=None alone a high-pass filter at fmin.
FilterBankLeftRightImagery applies a bank of bands, set with its filters argument.

To get the signal with no paradigm preprocessing at all, load it from the dataset object (dataset.get_data()) instead of from the paradigm.

References

[1]

S. Chevallier, I. Carrara, B. Aristimunha, P. Guetschel, S. Sedlar, B. Lopes, S. Velut, S. Khazem and T. Moreau. The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark. arXiv:2404.15319 (2024). https://arxiv.org/abs/2404.15319

[2]

C. S. Nam, A. Nijholt and F. Lotte (eds.). Brain-Computer Interfaces Handbook: Technological and Theoretical Advances. CRC Press (2018).

[3]

V. Jayaram and A. Barachant. MOABB: trustworthy algorithm benchmarking for BCIs. Journal of Neural Engineering 15(6), 066011 (2018). https://doi.org/10.1088/1741-2552/aadea0

property scoring#: Property that defines scoring metric (e.g. ROC-AUC or accuracy or f-score), given as a sklearn-compatible string or a compatible sklearn scorer.