期刊
PATTERN RECOGNITION
卷 45, 期 6, 页码 2123-2136出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.04.034
关键词
BCI; Phase synchronization; Functional connectivity; Complex networks; Finger tapping; HMM
资金
- EPSRC [EP/F033036/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/F033036/1] Funding Source: researchfish
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.
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