Title :
Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition
Author :
Cheolsoo Park ; Looney, David ; ur Rehman, Naveed ; Ahrabian, Alireza ; Mandic, Danilo P.
Author_Institution :
Dept. of Bioeng., Univ. of California-San Diego, La Jolla, CA, USA
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 and a well established BCI motor imagery dataset support the analysis.
Keywords :
bioelectric phenomena; brain-computer interfaces; electroencephalography; feature extraction; frequency estimation; medical signal processing; neurophysiology; signal classification; signal denoising; BCI motor imagery dataset; EEG; brain electrical activity; brain-computer interface; comparative analysis; data nonstationarity; electroencephalogram; feature extraction; frequency bands; highly localized time-frequency representation; low signal-noise ratio; motor imagery BCI classification; multivariate empirical mode decomposition; multivariate extensions; standard frequency estimation algorithms; state of the art methods; synthetic benchmark; Electroencephalography; Frequency estimation; Indexes; Noise; Standards; Time frequency analysis; Brain–computer interface (BCI); electroencephalogram (EEG); empirical mode decomposition; motor imagery paradigm; noise assisted multivariate extensions of empirical mode decomposition (NA-MEMD); Algorithms; Brain Mapping; Brain-Computer Interfaces; Electroencephalography; Evoked Potentials, Motor; Humans; Imagination; Motor Cortex; Movement; Pattern Recognition, Automated;
Journal_Title :
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
DOI :
10.1109/TNSRE.2012.2229296