Takagi-Sugeno-Kang transfer learning fuzzy logic system for the adaptive recognition of epileptic electroencephalogram signals

Changjian Yang, Zhaohong Deng, Kup Sze Choi, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)


� 2015 IEEE. 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 dealingwith situationswhere the training and test datasets differ with regard to data distribution.
Original languageEnglish
Article number7331145
Pages (from-to)1079-1094
Number of pages16
JournalIEEE Transactions on Fuzzy Systems
Issue number5
Publication statusPublished - 1 Oct 2016


  • Distribution diversity, electroencephalogram (EEG)
  • Epilepsy detection
  • Feature extraction
  • Takagi-Sugeno- Kang (TSK) fuzzy logic system (FLS)
  • Transfer learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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