Towards physiology-informed data augmentation for EEG-based BCIs
This addresses the need for reduced calibration data in BCIs for users with motor impairments, though it appears incremental as it builds on existing data augmentation techniques with a physiological twist.
The paper tackles the problem of high data requirements for training EEG-based Brain-Computer Interfaces due to variability in EEG data, by proposing a physiology-informed data augmentation method that uses source localization and head models to generate inter-participant variability, resulting in accuracy improvements of up to 13 percentage points for participant-independent motor-imagery classification.
Most EEG-based Brain-Computer Interfaces (BCIs) require a considerable amount of training data to calibrate the classification model, owing to the high variability in the EEG data, which manifests itself between participants, but also within participants from session to session (and, of course, from trial to trial). In general, the more complex the model, the more data for training is needed. We suggest a novel technique for augmenting the training data by generating new data from the data set at hand. Different from existing techniques, our method uses backward and forward projection using source localization and a head model to modify the current source dipoles of the model, thereby generating inter-participant variability in a physiologically meaningful way. In this manuscript, we explain the method and show first preliminary results for participant-independent motor-imagery classification. The accuracy was increased when using the proposed method of data augmentation by 13, 6 and 2 percentage points when using a deep neural network, a shallow neural network and LDA, respectively.