Learning Advanced Brain Computer Interface Technology
| Autor: | Zhang Weiran, Wu Linyan, Li Yanping, Gao Nuo, Wang Tao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
020203 distributed computing
business.industry Computer science Extraction (chemistry) Pattern recognition 02 engineering and technology 030204 cardiovascular system & hematology Human-Computer Interaction 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Artificial intelligence EEG feature business Information Systems Brain–computer interface |
| Zdroj: | International Journal of Technology and Human Interaction. 15:14-27 |
| ISSN: | 1548-3916 1548-3908 |
| Popis: | Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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