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Comparison of Adaptive Spatial Filters with Heuristic and Optimized Region of Interest for EEG Based Brain-Computer-Interfaces

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84055

Liefhold,  C
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83948

Grosse-Wentrup,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Liefhold, C., Grosse-Wentrup, M., Gramann, K., & Buss, M. (2007). Comparison of Adaptive Spatial Filters with Heuristic and Optimized Region of Interest for EEG Based Brain-Computer-Interfaces. Pattern Recognition: 29th DAGM Symposium, 274-283.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CBD9-3
Abstract
Research on EEG based brain-computer-interfaces (BCIs) aims at steering devices by thought. Even for simple applications, BCIs require an extremely effective data processing to work properly because of the low signal-to-noise-ratio (SNR) of EEG signals. Spatial filtering is one successful preprocessing method, which extracts EEG components carrying the most relevant information. Unlike spatial filtering with Common Spatial Patterns (CSP), Adaptive Spatial Filtering (ASF) can be adapted to freely selectable regions of interest (ROI) and with this, artifacts can be actively suppressed. In this context, we compare the performance of ASF with ROIs selected using anatomical a-priori information and ASF with numerically optimized ROIs. Therefore, we introduce a method for data driven spatial filter adaptation and apply the achieved filters for classification of EEG data recorded during imaginary movements of the left and right hand of four subjects. The results show, that in the case of artifact-free datasets, ASFs with numerically optimized ROIs achieve classification rates of up to 97.7 while ASFs with ROIs defined by anatomical heuristic stay at 93.7 for the same data. Otherwise, with noisy datasets, the former brake down (66.7 ) while the latter meet 95.7 .