ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)||6|
|Section||Algorithm Optimization and Application|
|Published online||23 June 2022|
Filter bank riemannian-based kernel support vector machine for motor imagery decoding
1 Fan Gongxiu Honors College, Beijing University of Technology, Beijing, China
2 Faculty of Information Technology, Beijing University of Technology, Beijing, China
* Corresponding author: firstname.lastname@example.org
Brain computer interface (BCI) enables the communication between the brain and external machines through Electroencephalography (EEG) signals, which has attracted lots of attention. Motor Imagery-based BCI (MI-BCI) is one of the most important paradigms in the BCI field. In MI-BCI, machine learning algorithms can be employed for identifying the target limb of motor intention effectively. As a typical machine learning algorithm for motor imagery decoding, the Riemannian-based kernel support vector machine (RK-SVM) algorithm is not capable of feature extraction from multiple frequency bands, which limits its performance. To solve this problem, the Filter Bank Riemannian-based Kernel Support Vector Machine (FBRK-SVM) method that combines the filter bank structure and Riemannian-based kernel was proposed. In comparative experiments on two commonly used public datasets, it is found that the proposed algorithm can yield higher decoding performance, which provides a new option for the classification of motor imagery.
Key words: Brain computer interface / Motor imagery / Riemannian geometry / Machine learning
© The Authors, published by EDP Sciences, 2022
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