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 language | English |
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Title of host publication | 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISBN (Electronic) | 9781509060344 |
DOIs | |
Publication status | Published - 24 Aug 2017 |
Externally published | Yes |
Event | IEEE International Conference on Fuzzy Systems 2017 - Naples, Italy Duration: 9 Jul 2017 → 12 Jul 2017 Conference number: 26th https://ieeexplore.ieee.org/xpl/conhome/8011834/proceeding (Proceedings) |
Conference
Conference | IEEE International Conference on Fuzzy Systems 2017 |
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Abbreviated title | FUZZ-IEEE 2017 |
Country/Territory | Italy |
City | Naples |
Period | 9/07/17 → 12/07/17 |
Internet address |