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Title: Takagi-Sugeno-Kang transfer learning fuzzy logic system for the adaptive recognition of epileptic electroencephalogram signals
Authors: Yang, CJ
Deng, ZH
Choi, KS 
Wang, ST
Issue Date: Oct-2016
Source: IEEE transactions on fuzzy systems, Oct. 2016, v. 24, no. 5, p. 1079-1094
Abstract: The intelligent recognition of electroencephalogram (EEG) signals has become an important approach to the detection of epilepsy. Among existing intelligent identification methods, fuzzy logic systems (FLSs) have shown a distinctive advantage in identifying epileptic EEG signals because of their strong learning abilities and interpretability. Like many conventional intelligent methods for recognizing EEG signals, in the training of FLS, it is assumed that the training dataset and test dataset are drawn from data that are identically distributed. However, this assumption is not necessarily valid in practice as it is not uncommon for the two datasets to have different distributions. To overcome this problem, a strategy is presented in this paper to construct a Takagi-Sugeno-Kang (TSK) FLS based on transductive transfer learning for identifying epileptic EEG signals. Two novel objective functions, achieved by integrating the transductive transfer learning mechanism, are proposed for the training of the TSK FLS. As regression and binary classification are two common approaches to multiclass classification, the TSK transfer learning FLS algorithms for regression and binary classification are developed, respectively, to construct the corresponding TSK FLS. Both algorithms are further used to perform a multiclass classification to recognize epileptic EEG signals. Their performance in the epileptic EEG datasets indicates promise in dealing with situations where the training and test datasets differ with regard to data distribution.
Keywords: Distribution diversity
Electroencephalogram (EEG)
Epilepsy detection
Feature extraction
Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS)
Transfer learning
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on fuzzy systems 
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2015.2501438
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication C. Yang, Z. Deng, K. Choi and S. Wang, "Takagi–Sugeno–Kang Transfer Learning Fuzzy Logic System for the Adaptive Recognition of Epileptic Electroencephalogram Signals," in IEEE Transactions on Fuzzy Systems, vol. 24, no. 5, pp. 1079-1094, Oct. 2016 is available at
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