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Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks

Park, C, , Cheong Took, C and Mandic D. P., (2013) Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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Abstract

A novel augmented complex-valued common spatial pattern (CSP) algorithm is introduced in order to cater for general complex signals with noncircular probability distributions. This is a typical case in multichannel electroencephalogram (EEG), due to the power difference or correlation between the data channels, yet current methods only cater for a very restrictive class of circular data. The proposed complex-valued CSP algorithms account for the generality of complex noncircular data, by virtue of the use of augmented complex statistics and the strong-uncorrelating transform (SUT). Depending on the degree of power difference of complex signals, the analysis and simulations show that the SUT based algorithm maximizes the inter-class difference between two motor imagery tasks. Simulations on both synthetic noncircular sources and motor imagery experiments using real-world EEG support the approach.

Item Type: Article
Authors :
NameEmailORCID
Park, C, UNSPECIFIEDUNSPECIFIED
Cheong Took, CUNSPECIFIEDUNSPECIFIED
Mandic D. P., UNSPECIFIEDUNSPECIFIED
Date : 12 December 2013
Identification Number : 10.1109/TNSRE.2013.2294903
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/EDTSajda, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 10:54
Last Modified : 28 Mar 2017 10:54
URI: http://epubs.surrey.ac.uk/id/eprint/808094

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