Connectivity based feature-level filtering for single-trial EEG BCIs

EEG-based Brain Computer interfaces (BCIs) often rely on power spectral density features to represent relevant aspects of brain activity. The information flow within human brain networks and the corresponding connectivity patterns may contain useful information to improve BCI performance, however they are typically not leveraged in current systems. In this paper, analyzes of information flow between independent sources of brain activity have been incorporated into the feature extraction stage of a BCI. For this purpose, connectivity measures based on multivariate autoregressive models have been estimated and are applied as filters to power spectral density based features. Two publicly available data sets have been used to evaluate the proposed feature extraction method: a two-back task and a motor imagery task. The results demonstrate significant performance improvements of the proposed method over band-power features and indicate that connectivity in brain networks can be used as powerful feature-level filters for BCIs.