Multi-task logistic regression in brain-computer interfaces

A brain-computer interface (BCI) is used to enable communication between humans and machines by decoding elicited brain activity patterns. However, these patterns have been found to vary across subjects or even for the same subject across sessions. Such problems render the performance of a BCI highly specific to subjects, requiring expensive and time-consuming individual calibration sessions to adapt BCI systems to new subjects. This work tackles the aforementioned problem in a Bayesian multi-task learning (MTL) framework to transfer common knowledge across subjects and sessions for the adaptation of a BCI to new subjects. In particular, a recent framework, that is able to exploit the structure of multi-channel electroencephalography (EEG), is extended by a Bayesian hierarchical logistic regression decoder for probabilistic binary classification. The derived model is able to explicitly learn spatial and spectral features, therefore making it further applicable for identification, analysis and evaluation of paradigm characteristics without relying on expert knowledge. An offline experiment with the new decoder shows a significant improvement in performance on calibration-free decoding compared to previous MTL approaches for rule adaptation and uninformed models while also outperforming them as soon as subject-specific data becomes available. We further demonstrate the ability of the model to identify relevant topographies along with signal band-power features that agree with neurophysiological properties of a common sensorimotor rhythm paradigm.