期刊
SENSORS
卷 22, 期 19, 页码 -出版社
MDPI
DOI: 10.3390/s22197596
关键词
electroencephalogram; adaptive classifier; support vector machine; common spatial pattern; online recursive independent component analysis
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- New Brunswick Innovation Foundation (NBIF)
In this paper, a feature extraction method based on ORICA-CSP is proposed for two classes of EEG experiments. The experimental results show that the ORICA-CSP method with A-SVM classifier has good performance.
An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain-computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes' motor images, namely Dataset 2a of BCI Competition IV.
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