Identification of Epileptic EEG Signals Through TSK Transfer Learning Fuzzy System

We propose a new model to identify epilepsy EEG signals. Some existing intelligent recognition technologies require that the training set and test set have the same distribution when recognizing EEG signals, some only consider reducing the marginal distribution distance of the data while ignoring th...

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Vydané v:Frontiers in neuroscience Ročník 15; s. 738268
Hlavní autori: Zheng, Zhaoliang, Dong, Xuan, Yao, Jian, Zhou, Leyuan, Ding, Yang, Chen, Aiguo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Research Foundation 10.09.2021
Frontiers Media S.A
Predmet:
Algorithms
Datasets
EEG
Epilepsy
epilepsy EEG signals
Feature selection
Fuzzy sets
interpretability
knowledge learning
Machine learning
Neuroscience
Probability distribution
Regression analysis
Transfer learning
TSK fuzzy system
transfer learning
interpretability
epilepsy EEG signals
knowledge learning
TSK fuzzy system
ISSN:1662-453X, 1662-4548, 1662-453X
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Shrnutí:We propose a new model to identify epilepsy EEG signals. Some existing intelligent recognition technologies require that the training set and test set have the same distribution when recognizing EEG signals, some only consider reducing the marginal distribution distance of the data while ignoring the intra-class information of data, and some lack of interpretability. To address these deficiencies, we construct a TSK transfer learning fuzzy system (TSK-TL) based on the easy-to-interpret TSK fuzzy system the transfer learning method. The proposed model is interpretable. By using the information contained in the source domain and target domains more effectively, the requirements for data distribution are further relaxed. It realizes the identification of epilepsy EEG signals in data drift scene. The experimental results show that compared with the existing algorithms, TSK-TL has better performance in EEG recognition of epilepsy.
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Edited by: Yuanpeng Zhang, Nantong University, China
This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience
Reviewed by: Yanhui Zhang, Hebei University of Chinese Medicine, China; Min Shi, Fuzhou University of International Studies and Trade, China
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2021.738268