Classifying EEG for brain-computer interfaces

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Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features

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ACM International Conference Proceeding Series; Vol. 148 archive
Proceedings of the 23rd international conference on Machine learning table of contents

Pittsburgh, Pennsylvania

Pages: 857 - 864

Year of Publication: 2006

ISBN:1-59593-383-2

Authors

Le Song	the University of Sydney, N.S.W., Australia and National ICT Australia, Alexandria, N.S.W., Australia
Julien Epps	the University of Sydney, N.S.W., Australia and National ICT Australia, Alexandria, N.S.W., Australia

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ACM New York, NY, USA

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Downloads (6 Weeks): 21, Downloads (12 Months): 101, Citation Count: 4

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ABSTRACT

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 novel features from the collective dynamics of the 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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 4

	Le Song , Julien Epps, Classifying EEG for brain-computer interface: learning optimal filters for dynamical system features, Computational Intelligence and Neuroscience, v.7 n.3, p.1-11, August 2007
	Suogang Wang , Christopher J. James, Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis, Computational Intelligence and Neuroscience, v.2007 n.1, p.6-6, January 2007
	W. L. Woon , A. Cichocki, Novel features for brain-computer interfaces, Computational Intelligence and Neuroscience, v.2007 n.1, p.10-10, January 2007
	Le Song , Julien Epps, Classifying EEG for brain-computer interface: learning optimal filters for dynamical system features, Computational Intelligence and Neuroscience, v.7 n.4, p.1-11, January 2007