EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

Journal of Neural Engineering
Vikram Shenoy HandiruCuntai Guan

Abstract

In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space E...Continue Reading

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Citations

Jul 12, 2018·IEEE Transactions on Bio-medical Engineering·Seyede Mahya SafaviPai H Chou
Nov 14, 2020·Disability and Rehabilitation. Assistive Technology·Mahdie Khaliq FardAli Maleki

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