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 %.
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References
Pfurtscheller, G., Lopes, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology 110, 1842–1857 (1999)
Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology 103, 642–651 (1997)
Ramoser, H., Mueller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering 8(4), 441–446 (2000)
Blanchard, G., Blankertz, G.: BCI competition 2003 - data set IIa: Spatial patterns of self-controlled brain rythm modulations. IEEE Transactions on Biomedical Engineering 51(6), 1062–1066 (2004)
Grosse-Wentrup, M., Gramann, K., Buss, M.: Adaptive spatial filters with predefined region of interest for EEG based brain-computer-interfaces. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, MIT Press, Cambridge, MA (2007)
Cuffin, B.N., Cohen, D.: Comparison of the magnetoencephalogram and electroencephalogram. Electroencephalography and Clinical Neurophysiology 47(2), 132–146 (1979)
Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation 11, 417–441 (1999)
Delorme, A., Makeig, S.: EEGlab: an open source toolbox for analysis of singletrial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134(1), 9–21 (2004)
Nunez, P.L., Shrinivasan, R.: Electric Fields of the Brain. In: The Neurophysics of EEG, 2nd edn., Oxford University Press, Oxford (2006)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2000)
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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. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_28
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DOI: https://doi.org/10.1007/978-3-540-74936-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74933-2
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