Name
Abstract | ||
|---|---|---|
The research of EEG classification is of great significance to the application and development of brain-computer interface. The realization of brain-computer interface depends on the good accuracy and robustness of EEG classification. Because the brain electrical capacitance is susceptible to the interference of noise and other signal sources (EMG, EEG, ECG, etc.), EEG classifier is difficult to improve the accuracy and has very low generalization ability. A novel method based on sparse autoencoder (SAE) and convolutional neural network (CNN) is proposed for feature extraction and classification of motor imagery electroencephalogram (EEG) signals. The performance of the proposed method is evaluated with real EEG signals from different subjects. The experimental results show that the network structure can get better classification results than other classification algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-91189-2_26 | ARTIFICIAL INTELLIGENCE AND ALGORITHMS IN INTELLIGENT SYSTEMS |
Keywords | Field | DocType |
Electroencephalography (EEG),Convolutional neural networks (CNN),Motion imaging,Sparse autoencoder (SAE) | Autoencoder,Pattern recognition,Convolutional neural network,Computer science,Feature extraction,Robustness (computer science),Artificial intelligence,Classifier (linguistics),Statistical classification,Electroencephalography,Motor imagery | Conference |
Volume | ISSN | Citations |
764 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianlun Tang | 1 | 7 | 4.86 |
| Jiwei Yang | 2 | 0 | 0.34 |
| Hui Wan | 3 | 2 | 2.99 |