Brain computer interface control via functional connectivity dynamics

Authors:
Ian Daly;Slawomir J. Nasuto;Kevin Warwick
Affiliations:
University of Reading, Reading, UK;University of Reading, Reading, UK;University of Reading, Reading, UK
Venue:
Pattern Recognition
Year:
2012

Citing 4
Cited 1

A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases

Pattern Recognition Letters
2009 Special Issue: The MindGame: A P300-based brain-computer interface game

Neural Networks
A wavelet tool to discriminate imagery versus actual finger movements towards a brain–computer interface

ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Design of a linguistic statistical decoder for the recognition of continuous speech

IEEE Transactions on Information Theory

Editorial: Brain decoding: Opportunities and challenges for pattern recognition

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.