Beamforming in non-invasive Brain-Computer Interfaces
Spatial filtering constitutes an integral part of building EEG-based Brain-Computer Interfaces (BCIs). Algorithms frequently used for spatial filtering, such as Common Spatial Patterns (CSP) and Independent Component Analysis (ICA), require labeled training data for identifying filters that provide information on a subjects intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, Beamforming is employed to construct spatial filters that extract EEG sources originating within pre-defined regions of interest (ROIs) within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering in a two-class motor-imagery paradigm. It is demonstrated that Beamforming outperforms CSP and Laplacian spatial filtering on noisy datasets, while CSP and Beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that Beamforming constitutes an alternative method for spatial filtering that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.