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Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system

Jiang, Y., Deng, Z., Chung, F-L, Wang, G.ORCID: 0000-0002-5258-0532, Qian, P., Choi, K-S and Wang, S. (2017) Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system. IEEE Transactions on Fuzzy Systems, 25 (1). pp. 3-20.

Link to Published Version: https://doi.org/10.1109/TFUZZ.2016.2637405
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Abstract

Recognition of epileptic electroencephalogram (EEG) signals using machine learning techniques is becoming popular. In general, the construction of intelligent epileptic EEG recognition system involves two steps. First, an appropriate feature extraction method is applied to obtain representative features from the original raw EEG signals. Second, an effective intelligent model is trained based on the extracted features. However, there exist two major challenges in the process: 1) it is nontrivial to determine the appropriate feature extraction method to be used; 2) although many classical machine learning methods have been used for epileptic EEG recognition, most of them are “black box” approaches and more interpretable methods are desirable. To address these two challenges, a new epileptic EEG recognition method based on a multiview learning framework and fuzzy system modeling is proposed. First, multiview EEG data are generated by employing different feature extraction methods to obtain the features from different views of the signals. Second, the classical Takagi-Sugeno-Kang fuzzy system (TSK-FS) is introduced as an easy-to-interpret recognition model to develop a multiview TSK-FS method, called MV-TSK-FS, to identify epileptic EEG signals. For the proposed MV-TSK-FS, the importance of each view, i.e., the importance of each feature extraction method, can be evaluated according to the weighting of each view, and consequently the final decision can be made based on the weighted outputs of different views. Experimental results indicate that the MV-TSK-FS is a promising method when compared with the state-of-the-art algorithms.

Item Type: Journal Article
Publisher: IEEE
Copyright: © 2016 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/52809
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