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

Authors:
Le Song;Julien Epps
Affiliations:
School of Information Technologies, The University of Sydney, NSW, Australia;National ICT Australia, Alexandria, NSW, Australia
Venue:
Computational Intelligence and Neuroscience - Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications
Year:
2007

Citing 2
Cited 0

A brain computer interface with online feedback based on magnetoencephalography

ICML '05 Proceedings of the 22nd international conference on Machine learning
Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features

ICML '06 Proceedings of the 23rd international conference on Machine learning

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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 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.