Enhanced detection of movement onset in EEG through deep oversampling

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.