Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
Lengthy subject- or session-specific data acquisition and calibration remain
a key barrier to deploying electroencephalography (EEG)-based brain-computer
interfaces (BCIs) outside the laboratory. Previous work has shown that cross
subject, cross-session invariant features exist in EEG. We propose a transfer
learning pipeline based on a two-layer convolutional neural network (CNN) that
leverages these invariants to reduce the burden of data acquisition and
calibration. A baseline model is trained on EEG data from five able-bodied
individuals and then rapidly updated with a small amount of data from a sixth,
holdout subject. The remaining holdout data were used to test the performance
of both the baseline and updated models. We repeated this procedure via a
leave-one-subject out (LOSO) validation framework. Averaged over six LOSO
folds, the updated model improved classification accuracy upon the baseline by
10.0, 18.8, and 22.1 percentage points on two binary and one ternary
classification tasks, respectively. These results demonstrate that decoding
accuracy can be substantially improved with minimal subject-specific data. They
also indicate that a CNN-based decoder can be personalized rapidly, enabling
near plug-and-play BCI functionality for neurorehabilitation and other
time-critical EEG applications.
