Compact and interpretable convolutional neural network architecture for electroencephalogram based motor imagery decoding
Recently, due to the popularity of deep learning, the applicability of deep Neural Networks (DNN) algorithms such as the convolutional neural networks (CNN) has been explored in decoding electroencephalogram (EEG) for Brain-Computer Interface (BCI) applications. This allows decoding of the EEG signa...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/101969/1/TarmiziAhmadIzzuddinPSKE2022.pdf.pdf http://eprints.utm.my/id/eprint/101969/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149285 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|