Multi-class EEG classification of voluntary hand movement directions

Multi-class EEG classification of voluntary hand movement directions
Multi-class EEG classification of voluntary hand movement directions
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Journal of Neural Engineering
Publication Date:
10 September 2013
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.
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.
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