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Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG
- Source :
- Cogn Neurodyn
- Publication Year :
- 2020
-
Abstract
- Deep learning has achieved great success in areas such as computer vision and natural language processing. In the past, some work used convolutional networks to process EEG signals and reached or exceeded traditional machine learning methods. We propose a novel network structure and call it QNet. It contains a newly designed attention module: 3D-AM, which is used to learn the attention weights of EEG channels, time points, and feature maps. It provides a way to automatically learn the electrode and time selection. QNet uses a dual branch structure to fuse bilinear vectors for classification. It performs four, three, and two classes on the EEG Motor Movement/Imagery Dataset. The average cross-validation accuracy of 65.82%, 74.75%, and 82.88% was obtained, which are 7.24%, 4.93%, and 2.45% outperforms than the state-of-the-art, respectively. The article also visualizes the attention weights learned by QNet and shows its possible application for electrode channel selection.
- Subjects :
- Artificial neural network
Computer science
business.industry
Cognitive Neuroscience
Deep learning
05 social sciences
Process (computing)
Bilinear interpolation
Pattern recognition
Convolutional neural network
050105 experimental psychology
03 medical and health sciences
0302 clinical medicine
Motor imagery
Feature (computer vision)
0501 psychology and cognitive sciences
Artificial intelligence
business
030217 neurology & neurosurgery
Decoding methods
Research Article
Subjects
Details
- ISSN :
- 18714080
- Volume :
- 15
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Cognitive neurodynamics
- Accession number :
- edsair.doi.dedup.....155813249e773bccf6d8da79c9177af2