Title: EEG single-trial classification of different motor imagery tasks using measures of dispersion and power in frequency bands

Authors: Yusuf U. Khan; Francisco Sepulveda

Addresses: Department of Electrical Engineering, A. M. University, Aligarh 202002, India ' Brain Computer Interface Research Group, Department of Computing and Electronic Systems, University of Essex, CO4 3SQ, UK

Abstract: This paper proposes a novel combination of features for the four types of wrist movement discrimination (extension, flexion, pronation and supination) on left and right wrist respectively. The features are based on dispersion measures (Inter Quartile Range (IQR) and Median Absolute Deviation (MAD)), entropy and band power in the EEG signal. These features are input to the RBF classifier to test classification accuracy. The classification rate was up to 92% with an average of over 90% in the four subjects. The reduced computational intricacy and the resulting acceleration in speed obtained were other hallmarks of this method. These results show further improvement in recognition rate when compared with the groups earlier effort on same database (Khan and Sepulveda, 2010; Hubais et al., 2006).

Keywords: band power; entropy; IQR; inter quartile range; MAD; median absolute deviation; EEG; wrist movement; radial basis function; RBF neural networks; electroencephalograms; extension; flexion; pronation; supination.

DOI: 10.1504/IJBET.2012.046960

International Journal of Biomedical Engineering and Technology, 2012 Vol.8 No.4, pp.343 - 356

Published online: 12 Dec 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article