1. A motor imagery EEG signal classification algorithm based on recurrence plot convolution neural network.
- Author
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Meng, XianJia, Qiu, Shi, Wan, Shaohua, Cheng, Keyang, and Cui, Lei
- Subjects
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CONVOLUTIONAL neural networks , *SIGNAL classification , *BRAIN-computer interfaces , *CLASSIFICATION algorithms , *ELECTROENCEPHALOGRAPHY , *BRAINWASHING - Abstract
• Limited information in time domain results in limited performance of feature classification. • The particularity of EEG signal makes it difficult to measure. • The strong correlation of EEG signals makes it difficult to build feature extraction network. With the promotion of brain-computer interface technology, it is possible to study brain control system through EEG signals in recent years. In order to solve the problem of EEG signal classification effectively, a motor imagery classification algorithm based on recurrence plot convolution neural network is proposed. Firstly, EEG signals are preprocessed to enhance the signal intensity in the exercise interval. Secondly, time-domain and frequency-domain features are extracted respectively to construct the feature mode of recurrence plot. Finally, a new neural network is established to realize the accurate recognition of left and right movements. This research can also be transferred to other research fields. [Display omitted] [ABSTRACT FROM AUTHOR]
- Published
- 2021
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