  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.
