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Brain computer interface control via functional connectivity dynamics
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DOI：10.1016/j.patcog.2011.04.034.png)
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Abstract
En 中文
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 EEC. 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 EEC 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. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
BCI
Phase synchronization
Functional connectivity
Complex networks
Finger tapping
HMM
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