A Novel Mechanism to Fuse Various Sub-aspect Brain-computer Interface (BCI) Systems with PSO for Motor Imagery Task

Chin-Teng Lin, Tsung Yu Hsieh, Yu-Ting Liu, Shang-Lin Wu, Yang-Yin Lin

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

3 Scopus citations

Abstract

In this study, we develop a novel multi-fusion brain-computer interface (BCI) system based on a fuzzy neural network (FNN) and information fusion approaches to cope with a classification task for identifying right/left hand motor imagery. In the proposed system, we utilize a filter bank and sub-band common spatial pattern (SBCSP) to extract features from raw EEG data. A self-organizing neural fuzzy inference network (SONFIN) is then applied for a recognition task. In order to improve the classification performance, we form a committee of networks and employ fuzzy integral (FI) to attain a joint decision. To further optimize the fusion approaches, a particle swarm optimization (PSO) algorithm is exploited to globally update parameters used in the fusion stage. In consequence, our experimental result shows that the proposed fuzzy fusion system possesses superior performance compared to other comparative models.
Original languageEnglish
Title of host publicationIEEE International Conference on Systems, Man, and Cybernetics (SMC)
PublisherIEEE
Pages3223-3228
Number of pages6
DOIs
StatePublished - 2015

Keywords

  • brain-computer interface (BCI); motor imagery (MI); sub-band common spatial pattern (SBCSP); fuzzy neural network (FNN); fuzzy integral (FI); particle swarm optimization (PSO)

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    Lin, C-T., Hsieh, T. Y., Liu, Y-T., Wu, S-L., & Lin, Y-Y. (2015). A Novel Mechanism to Fuse Various Sub-aspect Brain-computer Interface (BCI) Systems with PSO for Motor Imagery Task. In IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3223-3228). IEEE. https://doi.org/10.1109/SMC.2015.559