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

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3 Author(s)
Cheolsoo Park ; Dept. of Bioeng., Univ. of California-San Diego, La Jolla, CA, USA ; Clive Cheong Took ; Danilo P. Mandic

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.

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IEEE Transactions on Neural Systems and Rehabilitation Engineering  (Volume:22 ,  Issue: 1 )