Enhanced detection of movement onset in EEG through deep oversampling

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

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, May). Enhanced detection of movement onset in EEG through deep oversampling. Presented at 30th International Joint Conference on Neural Networks (IJCNN 2017), Anchorage, Alaska, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 30th International Joint Conference on Neural Networks (IJCNN 2017)
• 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 21, 2018
• 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
• Public URL: https://durham-repository.worktribe.com/output/1146296
• Related Public URLs: https://eprint.ncl.ac.uk/247964