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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4114-4120
Number of pages7
ISBN (Electronic)9781479914845
DOIs
StatePublished - 3 Sep 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

Keywords

  • electroencephalogram (EEG) channel reduction
  • hybrid brain computer interface (BCI)
  • Motor Imagery (MI)
  • Steady State Visually Evoked Potentials (SSVEP)

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    Ko, L-W., Lin, S. C., Song, M. S., & Komarov, O. (2014). Developing a few-channel hybrid BCI system by using motor imagery with SSVEP assist. In Proceedings of the International Joint Conference on Neural Networks (pp. 4114-4120). [6889901] (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889901