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

作者：Park Cheolsoo^*; Took Clive Cheong; Mandic Danilo P

来源：IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(1): 1-10.

DOI：10.1109/TNSRE.2013.2294903

• Augmented complex common spatial pattern (ACCSP)
• brain-computer interface (BCI)
• common spatial pattern (CSP)
• complex noncircularity
• complex pseudocovariance
• motor imagery paradigm
• strong-uncorrelating transform (SUT)

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.

• 出版日期2014-1