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Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition

By Cheolsoo Park, David Looney, Naveed Ur Rehman, Alireza Ahrabian and Danilo P. M

Abstract

Abstract—Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain–computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples andawellestablishedBCImotorimagery dataset support the analysis. Index Terms—Brain–computer interface (BCI), electroencephalogram (EEG), empirical mode decomposition, motor imagery paradigm, noise assisted multivariate extensions of empirical mode decomposition (NA-MEMD). I

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.2723
Provided by: CiteSeerX
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