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

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 o...

• Type: Conference
• Year: 2007
• Where: DAGM
• Authors: Christian Liefhold, Moritz Grosse-Wentrup, Klaus Gramann, Martin Buss
• Keywords: Adaptive Spatial Filtering, DAGM 2007, EEG Based Brain-computer-interfaces, Spatial