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J Neural Eng. 2013 Oct;10(5):056018. doi: 10.1088/1741-2560/10/5/056018. Epub 2013 Sep 10.

Multi-class EEG classification of voluntary hand movement directions.

Author information

1
School of Computer Engineering, Nanyang Technological University, Singapore.

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.

PMID:
24018330
DOI:
10.1088/1741-2560/10/5/056018
[Indexed for MEDLINE]

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