JBM  Vol.2 No.2 , April 2014
Robust Spatial Filters on Three-Class Motor Imagery EEG Data Using Independent Component Analysis
Abstract: Independent Component Analysis (ICA) was often used to separate movement related independent components (MRICs) from Electroencephalogram (EEG) data. However, to obtain robust spatial filters, complex characteristic features, which were manually selected in most cases, have been commonly used. This study proposed a new simple algorithm to extract MRICs automatically, which just utilized the spatial distribution pattern of ICs. The main goal of this study was to show the relationship between spatial filters performance and designing samples. The EEG data which contain mixed brain states (preparing, motor imagery and rest) were used to design spatial filters. Meanwhile, the single class data was also used to calculate spatial filters to assess whether the MRICs extracted on different class motor imagery spatial filters are similar. Furthermore, the spatial filters constructed on one subject’s EEG data were applied to extract the others’ MRICs. Finally, the different spatial filters were then applied to single-trial EEG to extract MRICs, and Support Vector Machine (SVM) classifiers were used to discriminate left hand、right-hand and foot imagery movements of BCI Competition IV Dataset 2a, which recorded four motor imagery data of nine subjects. The results suggested that any segment of finite motor imagery EEG samples could be used to design ICA spatial filters, and the extracted MRICs are consistent if the position of electrodes are the same, which confirmed the robustness and practicality of ICA used in the motor imagery Brain Computer Interfaces (MI-BCI) systems.
Cite this paper: Zhou, B. , Wu, X. , Zhang, L. , Lv, Z. and Guo, X. (2014) Robust Spatial Filters on Three-Class Motor Imagery EEG Data Using Independent Component Analysis. Journal of Biosciences and Medicines, 2, 43-49. doi: 10.4236/jbm.2014.22007.

[1]   Nicolas-Alonso, L.F. and Gomez-Gil, J. (2012) Brain Computer Interfaces: A Review. Sensors, 12, 1211-1279.

[2]   Zima, M., Tichavsky, P., Paul, K. and Kraj?a, V. (2012) Robust Removal of Short-Duration Artifacts in Long Neonatal EEG Recordings Using Wavelet-Enhanced ICA and Adaptive Combining of Tentative Reconstructions. Physiological Measurement, 33, N39.

[3]   Grandchamp, R., Braboszcz, C., Makeig, S. and Delorme, A. (2012) Stability of ICA Decomposition across Within- Subject EEG Datasets. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, California, 8-28.

[4]   Lee, T.W., Girolami, M. and Sejnowski, T.J. (1999) Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources. Neural Computation, 11, 417-441.

[5]   Wang, Y., Wang, Y.T. and Jung, T. P. (2012) Translation of EEG Spatial Filters from Resting to Motor Imagery Using Independent Component Analysis. PloS ONE, 7, e37665.

[6]   Brunner, C., Naeem, M., Leeb, R., Graimann, B. and Pfurtscheller, G. (2007) Spatial Filtering and Selection of Optimized Components in Four Class Motor Imagery EEG Data Using Independent Components Analysis. Pattern Recognition Letters, 28, 957-964.

[7]   Naeem, M., Brunner, C., Leeb, R., Graimann, B. and Pfurtscheller, G. (2006) Seperability of Four-Class Motor Imagery Data Using Independent Components Analysis. Journal of Neural Engineering, 3, 208.

[8]   Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F. and Arnaldi, B. (2007) A Review of Classification Algorithms for EEG-Based Brain-Computer Interfaces. Journal of Neural Engineering, 4, R1-R13.

[9]   Xue, Z., Li, J., Li, S. and Wan, B. (2006) Using ICA to Remove Eye Blink and Power Line Artifacts in EEG. IEEE 1st International Conference on Innovative Computing, Information and Control, 2006 ICICIC’06, 107-110.

[10]   De Vos, M., De Lathauwer, L. and Van Huffel, S. (2011) Spatially Constrained ICA Algorithm with an Application in EEG Processing. Signal Processing, 91, 1963-1972.