Multi-class EEG classification of voluntary hand movement directions

Multi-class EEG classification of voluntary hand movement directions
Title:
Multi-class EEG classification of voluntary hand movement directions
Other Titles:
Journal of Neural Engineering
Publication Date:
10 September 2013
Citation:
Robinson, N., Guan, C., Vinod, A. P., Ang, K. K., & Tee, K. P. (2013). Multi-class EEG classification of voluntary hand movement directions. J. Neural Eng., 10(5), 056018.
Abstract:
Objective. Studies have shown that low frequency components of brain recordings provide information on voluntary hand movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. Approach. This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary hand movement directions. The proposed technique comprises the regularized wavelet-common spatial pattern algorithm to extract the features, mutual information-based feature selection, and multi-class classification using the Fisher linear discriminant. EEG data from seven healthy human subjects were collected while they performed voluntary right hand center-out movement in four orthogonal directions. In this study, the movement direction dependent signal-to-noise ratio is used as a parameter to denote the effectiveness of each temporal frequency bin in the classification of movement directions. Main results. Significant (p < 0.005) movement direction dependent modulation in the EEG data was identified largely towards the end of movement at low frequencies (≤6 Hz) from the midline parietal and contralateral motor areas. Experimental results on single trial classification of the EEG data collected yielded an average accuracy of (80.24 ± 9.41)% in discriminating the four different directions using the proposed technique on features extracted from low frequency components. Significance. The proposed feature extraction strategy provides very high multi-class classification accuracies, and the results are proven to be more statistically significant than existing methods. The results obtained suggest the possibility of multi-directional movement classification from single-trial EEG recordings using the proposed technique in low frequency components.
License type:
PublisherCopyrights
Funding Info:
Description:
ISSN:
1741-2560
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