PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface
Le Song, Elly Gysels and Evian Gordon
In: Advances in Neural Information Processing Systems 18, 5-8 Dec 2005, Vancouver, Canada.

Abstract

Motor imagery attenuates EEG mu and beta rhythms over sensorimotor cortices. These amplitude changes are most successfully captured by the method of Common Spatial Patterns (CSP) and widely used in brain-computer interfaces(BCI). BCI methods based on amplitude information, however, have not incoporated the rich phase dynamics in the EEG rhythm. This study reports on a BCI method based on phase synchrony rate (SR). SR, computed from binarized phase locking value, describes the number of discrete synchronization events within a window. Statistical nonparametric tests show that SRs contain significant differences between 2 types of motor imageries. Classifiers trained on SRs consistently demonstrate satisfactory results for all 5 subjects. It is further observed that, for 3 subjects, phase is more discriminative than amplitude in the first 1.5-2.0 s, which suggests that phase has the potential to boost the information transfer rate in BCIs.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
ID Code:2078
Deposited By:Le Song
Deposited On:10 February 2006