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A Convolutional Neural Network Model for Decoding EEG signals in a Hand-Squeeze Task1

Authors :
Dean Freeston
Farhad Goodarzy
Ewan S. Nurse
Andi Partovi
Mark J. Cook
Philippa J. Karoly
Anthony N. Burkitt
David B. Grayden
Source :
2020 8th International Winter Conference on Brain-Computer Interface (BCI).
Publication Year :
2020
Publisher :
IEEE, 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, our model is not limited by factors such as optimal order for the EEG channels and it is patient-invariant. We further investigate the effects of network architecture on decoder performance and training time.

Details

Database :
OpenAIRE
Journal :
2020 8th International Winter Conference on Brain-Computer Interface (BCI)
Accession number :
edsair.doi...........b3cc6b5d3f0105dab9b953628e493182
Full Text :
https://doi.org/10.1109/bci48061.2020.9061666