Identification of Epileptic EEG Signals Through TSK Transfer Learning Fuzzy System - Zheng, Zhaoliang; Dong, Xuan; Yao, Jian; Zhou, Leyuan; Ding, Yang; Chen, Aiguo (Frontiers in Neuroscience, 2021)
We propose a new model to identify epilepsy EEG signals. Some existing intelligent recognition technologies require the training set and test set to be the same distribu-tion when identifying EEG signals, and some only consider reducing the marginal distribution distance of the data while ignoring the intra-class information of the data, and some lack 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. Through more effective use of the information contained in the source and target domains, the requirements for data distribution are further relaxed. It realizes the identification of epilepsy EEG signals in data drift scenarios. The experimental results indicate that TSK-TL has better performance in epilepsy EEG signals recognition compared with the existing algorithms.