A Method of Motor Imagery EEG Recognition Based on CNN-ELM
| Autor: | Chun-ning Song, Yong Sheng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
medicine.diagnostic_test Computer science business.industry Interface (computing) 0206 medical engineering Pattern recognition 02 engineering and technology Electroencephalography 020601 biomedical engineering Convolutional neural network Motor imagery 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business S transform Brain–computer interface Extreme learning machine |
| Zdroj: | 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET). |
| DOI: | 10.1109/ccet50901.2020.9213132 |
| Popis: | It is the key of brain-computer interface technology to extract electroencephalogram (EEG) data features effectively and classify them accurately. In view of the characteristics of non-stationarity and obvious time-frequency characteristics of motor imagery EEG signals, this paper proposes a method for recognition of motor imagery EEG signals based on S-transform time-frequency image combined with convolutional neural network (CNN) and extreme learning machine (ELM). In the BCI competition dataset, firstly, the S-transform time-frequency image of C3 and C4 electrode signals is obtained, and then the characteristic frequency bands are extracted from the time-frequency image for combination. Finally, the combined image is used as the input of neural network to realize the recognition of left-right hand motor imagery EEG signals. Experimental results show that this method is superior to the ordinary convolutional neural network. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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