Conference Proceedings

A Convolutional Neural Network Model for Decoding EEG signals in a Hand-Squeeze Task

A Partovi, F Goodarzy, ES Nurse, P Karoly, D Freeston, M Cook, AN Burkitt, D Grayden

8th International Winter Conference on Brain-Computer Interface, BCI 2020 | IEEE | Published : 2020

Abstract

Brain computer interfaces can help individuals with movement disabilities such as locked-in syndrome, tetraplegia and cerebral palsy by providing a direct interface between the brain and various external mobility and assistive devices, such as spellers, wheelchairs, and prosthetics. Inspired by the recent advancements in computer vision, in this paper, we investigate the use of convolutional neural networks in decoding brain signals and evaluate the performance of our models on an existing dataset of EEG signals acquired through a hand squeeze task. Our model outperforms other neural network models previously applied on this dataset both in terms of accuracy, and training speed. Moreover, ou..

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Grants

Awarded by National Health and Medical Research Council


Funding Acknowledgements

Research supported by NHMRC project grant 1148005