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Enhanced detection of movement onset in EEG through deep oversampling.

Al Moubayed, Noura and Hasan, Bashar Awwad Shiekh and McGough, Andrew Stephen (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. Piscataway: IEEE, pp. 71-78.


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:03 February 2017
Date deposited:21 March 2018
Date of first online publication:03 July 2017
Date first made open access:No date available

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