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Brain computer interface control via functional connectivity dynamics

Daly, I., Nasuto, S. J. and Warwick, K. (2012) Brain computer interface control via functional connectivity dynamics. Pattern Recognition, 45 (6). pp. 2123-2136. ISSN 0031-3203

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To link to this article DOI: 10.1016/j.patcog.2011.04.034

Abstract/Summary

The dynamics of inter-regional communication within the brain during cognitive processing – referred to as functional connectivity – are investigated as a control feature for a brain computer interface. EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity. Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:23203
Uncontrolled Keywords:BCI, phase synchronization, functional connectivity, complex networks, finger tapping, HMM
Publisher:Elsevier

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