Beamforming in Noninvasive Brain-Computer Interfaces

• Published in: IEEE transactions on biomedical engineering Vol. 56; no. 4; pp. 1209 - 1219
• Main Authors: Grosse-Wentrup, Moritz, Liefhold, Christian, Gramann, Klaus, Buss, Martin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Array signal processing; Beamforming; Brain computer interfaces; Brain Mapping - methods; common spatial patterns; Data mining; Electroencephalography; Electroencephalography - methods; Female; Humans; Independent component analysis; Information filtering; Information filters; Male; Motor Cortex - physiology; motor imagery; Pattern Recognition, Automated - methods; Reference Values; Rendering (computer graphics); Signal Processing, Computer-Assisted; spatial filtering; Spatial filters; Training data; User-Computer Interface
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2008.2009768

Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject's 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 predefined regions of interest 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 (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP 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 SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.