Multiclass EEG data classification using fuzzy systems

Thanh Nguyen, Imali Hettiarachchi, Abbas Khosravi, Syed Moshfeq Salaken, Asim Bhatti, Saeid Nahavandi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

20 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781509060344
DOIs
Publication statusPublished - 24 Aug 2017
Externally publishedYes
EventIEEE International Conference on Fuzzy Systems 2017 - Naples, Italy
Duration: 9 Jul 201712 Jul 2017
Conference number: 26th
https://ieeexplore.ieee.org/xpl/conhome/8011834/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Fuzzy Systems 2017
Abbreviated titleFUZZ-IEEE 2017
Country/TerritoryItaly
CityNaples
Period9/07/1712/07/17
Internet address

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