Neurophysiologically meaningful motor imagery EEG simulation with applications to data augmentation
Abstract: Motor imagery-based Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention due to their
potential usability in neurorehabilitation and neuroprosthetics. However, the accurate recognition of MI patterns
in electroencephalography signals (EEG) is hindered by several data-related limitations, which restrict the practical
utilization of these systems. Moreover, leveraging deep
learning (DL) models for MI decoding is challenged by the
difficulty of accessing user-specific MI-EEG data on large
scales. Simulated MI-EEG signals can be useful to address
these issues, providing well-defined data for the validation
of decoding models and serving as a data augmentation
approach to improve the training of DL models. While
substantial efforts have been dedicated to implementing
effective data augmentation strategies and model-based
EEG signal generation, the simulation of neurophysiologically plausible EEG-like signals has not yet been exploited
in the context of data augmentation. Furthermore, none
of the existing approaches have integrated user-specific
neurophysiological information during the data generation
process. Here, we present PySimMIBCI, a framework for
generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward
models. By means of PySimMIBCI, different user capabilities to control an MI-BCI can be simulated and fatigue
effects can be included in the generated EEG. Results
show that our simulated data closely resemble real data.
Moreover, a proposed data augmentation strategy based on
our simulated user-specific data significantly outperforms
other state-of-the-art augmentation approaches, enhancing
DL models performance by up to 15%.
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