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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46294

Title: Fuzzy Integral With Particle Swarm Optimization for a Motor-Imagery-Based Brain–Computer Interface
Authors: Shang-Lin Wu
Yu-Ting Liu
Tsung-Yu Hsieh
Yang-Yin Lin
Chih-Yu Chen
Chun-Hsiang Chuang
Chin-Teng Lin
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Brain–computer interface (BCI);electroencephalography (EEG);fuzzy integral;motor imagery (MI);particle swarm optimization (PSO)
Date: 2017-02
Issue Date: 2018-05-14T07:34:38Z
Publisher: IEEE Transactions on Fuzzy Systems
Abstract: Abstract:
A brain-computer interface (BCI) system using elec-troencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications.
Relation: 25(1),pp. 21-28
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46294
Appears in Collections:[資訊工程學系] 期刊論文

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