Recognition of Epileptic EEG Signals Using a Novel Multiview TSK Fuzzy System

Yizhang Jiang, Zhaohong Deng, Fu Lai Korris Chung, Guanjin Wang, Pengjiang Qian, Kup Sze Choi, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

102 Citations (Scopus)

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.
Original languageEnglish
Article number7778175
Pages (from-to)3-20
Number of pages18
JournalIEEE Transactions on Fuzzy Systems
Volume25
Issue number1
DOIs
Publication statusPublished - 1 Feb 2017

Keywords

  • Collaborative learning mechanism
  • epileptic EEG recognition
  • multiview learning
  • Takagi-Sugeno-Kang (TSK) fuzzy system
  • View-Weighted mechanism

ASJC Scopus subject areas

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

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