Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

132 Citations (Scopus)


Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.
Original languageEnglish
Pages (from-to)2270-2284
Number of pages15
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number12
Publication statusPublished - 1 Dec 2017


  • EEG recognition
  • Seizure classification
  • Semi-supervised learning
  • Transductive transfer learning
  • TSK fuzzy system

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

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