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Multiclass EEG data classification using fuzzy systems

This paper presents an approach to analysis of multiclass EEG data obtained from the brain computer interface (BCI) applications. The proposed approach comprises two stages including feature extraction using the common spatial pattern (CSP) and classification using fuzzy logic systems (FLS). CSP is used to extract significant features that are then fed into FLS as inputs for classification. The metaheuristic population-based particle swarm optimization method is used to train parameters of the FLS. The multiclass motor imagery dataset IIa from the BCI competition IV is used for experiments to highlight the superiority of the proposed approach against competing methods, which include linear discriminant analysis, naïve bayes, k-nearest neighbour, ensemble learning AdaBoost and support vector machine. Results from experiments show the great accuracy of the combination of CSP and FLS. Therefore, the proposed approach can be implemented effectively in the practical BCI systems, which would be helpful for people with impairments and rehabilitation.

History

Event

IEEE Computational Intelligence Society. Conference (2017 : Naples, Italy)

Pagination

1 - 6

Publisher

IEEE

Location

Naples, Italy

Place of publication

Piscataway, N.J.

Start date

2017-07-09

End date

2017-07-12

ISSN

1098-7584

ISBN-13

9781509060344

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2017, IEEE

Title of proceedings

FUZZ-IEEE 2017 : Proceedings of the IEEE International Conference on Fuzzy Systems