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Enhanced detection of movement onset in EEG through deep oversampling

Al Moubayed, Noura; Hasan, Bashar Awwad Shiekh; McGough, Andrew Stephen

Enhanced detection of movement onset in EEG through deep oversampling Thumbnail


Authors

Bashar Awwad Shiekh Hasan

Andrew Stephen McGough



Abstract

A deep learning approach for oversampling of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and the detection of movement onset during online Brain-Computer Interfaces in particular. Learning from self-paced EEG data is challenging mainly due to the highly imbalance nature of the data reducing the generalisation power of the classification model. Oversampling of the movement class enhances the overall accuracy of an onset detection system by over 17%, p <; 0.05, when tested on 12 subjects. Modelling the data using a deep neural network not only helps oversampling the movement class but also can help build a subject independent model of movement. In this work we present initial results on the applicability of this model.

Citation

Al Moubayed, N., Hasan, B. A. S., & McGough, A. S. (2017). Enhanced detection of movement onset in EEG through deep oversampling. In 2017 International Joint Conference on Neural Networks (IJCNN 2017) : Anchorage, Alaska, USA, 14-19 May 2017 (71-78). https://doi.org/10.1109/ijcnn.2017.7965838

Conference Name 30th International Joint Conference on Neural Networks (IJCNN 2017)
Conference Location Anchorage, Alaska, USA
Start Date May 14, 2017
End Date May 19, 2017
Acceptance Date Feb 3, 2017
Online Publication Date Jul 3, 2017
Publication Date Jul 3, 2017
Deposit Date May 17, 2017
Publicly Available Date Mar 28, 2024
Pages 71-78
Series ISSN 2161-4407
Book Title 2017 International Joint Conference on Neural Networks (IJCNN 2017) : Anchorage, Alaska, USA, 14-19 May 2017.
ISBN 9781509061839
DOI https://doi.org/10.1109/ijcnn.2017.7965838
Related Public URLs https://eprint.ncl.ac.uk/247964

Files

Accepted Conference Proceeding (2.3 Mb)
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