Multi-Rhythm Capsule Network Recognition Structure for Motor Imagery Classification
DOI:
https://doi.org/10.13052/jwe1540-9589.2039Keywords:
Capsule network, deep learning, brain machine interface, motor imagery, classificationAbstract
Existing machine learning methods for classification and recognition of EEG motor imagery usually suffer from reduced accuracy for limited training data. To address this problem, this paper proposes a multi-rhythm capsule network (FBCapsNet) that uses as little EEG information as possible with key features to classify motor imagery and further improves the classification efficiency. The network conforms to a small recognition model with only 3 acquisition channels but it can effectively use the limited data for feature learning. Based on the BCI Competition IV 2b data set, experimental results show that the proposed network can achieve 2.41% better performance than existing cutting-edge methods.
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