Brain-Computer Interfaces: Towards Practical Implementations and Potential ApplicationsView this Special Issue
Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features
Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach.
G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K.-R. Müller, “Optimizing spatio-temporal filters for improving brain-computer interfacing,” in Advances in Neural Information Processing Systems, vol. 18, pp. 315–322, MIT Press, Cambridge, Mass, USA, 2006.View at: Google Scholar
T. N. Lal, M. Schröder, N. J. Hill et al., “A brain computer interface with online feedback based on magnetoencephalography,” in Proceedings of the 22nd International Conference on Machine Learning (ICML '05), pp. 465–472, Bonn, Germany, August 2005.View at: Google Scholar
L. Song, E. Gysels, and E. Gordon, “Phase synchrony rate for the recognition of motor imagery in brain-computer interface,” in Advances in Neural Information Processing Systems, vol. 18, pp. 1265–1272, MIT Press, Cambridge, Mass, USA, 2006.View at: Google Scholar
L. Song and J. Epps, “Improving separability of EEG signals during motor imagery with an efficient circular Laplacian,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '06), vol. 2, pp. 1048–1051, Toulouse, France, May 2006.View at: Google Scholar
A. Pikovsky, M. Rosenblum, J. Kurths, and B. Chirikov, Synchronization: A Universal Concept in Nonlinear Sciences, Cambridge University Press, Cambridge, UK, 2003.
X. Gao, Z. Zhang, B. Hong, and S. Gao, http://ida.first.fraunhofer.de/projects/bci/competition_iii/results/berlin_IVa/YijunWang_desc.pdf.