Developing a few-channel hybrid BCI system by using motor imagery with SSVEP assist

Li-Wei Ko*, Shih Chuan Lin, Meng Shue Song, Oleksii Komarov

*Corresponding author for this work

研究成果: Conference contribution同行評審

6 引文 斯高帕斯(Scopus)

摘要

Generally, Steady-State Visually Evoked Potentials (SSVEP) has widely recognized advantages, like being easy to use, requiring little user training [1], while Motor Imagery (MI) is not easy to introduce for some subjects. This work introduces a hybrid brain-computer interface (BCI) combines MI and SSVEP strategies such an approach allows us to improve performance and universality of the system, and also the number of EEG electrodes from 32 to 3 in central area can increase the efficiency of EEG preprocessing to design an effective and easy way to use hybrid BCI system. In this study the Common Spatial Pattern (CSP) algorithm was introduced as a feature extraction method, which provides a high accuracy in event-related synchronization/desynchronization (ERS/ERD)-based BCL The four most common classifiers (KNNC, PARZENDC, LDC, SVC) were used for accuracy estimation. Results show that support vector classifier (SVC) and K-nearest-neighbor (KNN) classifier provide better performance than others, and it is possible to reach the same good accuracy using 3-channel (C3, Cz, C4) hybrid BCI system, as with usual 32-channel system.

原文English
主出版物標題Proceedings of the International Joint Conference on Neural Networks
發行者Institute of Electrical and Electronics Engineers Inc.
頁面4114-4120
頁數7
ISBN(電子)9781479914845
DOIs
出版狀態Published - 3 九月 2014
事件2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
持續時間: 6 七月 201411 七月 2014

出版系列

名字Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
國家China
城市Beijing
期間6/07/1411/07/14

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