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

Authors:
Le Song;Julien Epps
Affiliations:
the University of Sydney, N.S.W., Australia and National ICT Australia, Alexandria, N.S.W., Australia;the University of Sydney, N.S.W., Australia and National ICT Australia, Alexandria, N.S.W., Australia
Venue:
ICML '06 Proceedings of the 23rd international conference on Machine learning
Year:
2006

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

Computational Intelligence and Neuroscience - Regular issue
Classifying EEG for brain-computer interface: learning optimal filters for dynamical system features

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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.