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Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification
Karel Roots
, Yar Muhammad
, Naveed Muhammad
Department of Computing & Games
Research output
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peer-review
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Dive into the research topics of 'Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification'. Together they form a unique fingerprint.
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Keyphrases
Brain-computer Interface
100%
Electroencephalogram
100%
Motor Imagery Classification
100%
Cross-subject
100%
Cross-subject Classification
100%
EEGNet
100%
Fusion Convolutional Neural Networks
100%
External Assistance
50%
Motor Action
50%
Computational Cost
50%
Three-state
50%
Low Computational Complexity
50%
Classification Methods
50%
Motor Abilities
50%
Electroencephalography
50%
Hyperparameters
50%
Deep Neural Network Classifier
50%
Interface Development
50%
Motor Imagery Data
50%
Interface Research
50%
2D Convolutional Neural Network
50%
Multi-branch
50%
Engineering
Motor Imagery
100%
Convolutional Neural Network
100%
Computational Cost
66%
Classification Method
33%
Motor Action
33%
Computer Science
Convolutional Neural Network
100%
Computational Cost
66%
Classification Method
33%
Interface Development
33%
Leaning Parameter
33%