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A Convolutional Neural Network Model for Decoding EEG signals in a Hand-Squeeze Task1
- 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.
- Subjects :
- Network architecture
Artificial neural network
medicine.diagnostic_test
business.industry
Computer science
Speech recognition
Deep learning
Interface (computing)
Electroencephalography
Convolutional neural network
medicine
Artificial intelligence
business
Decoding methods
Brain–computer interface
Subjects
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