Identification of Epileptic EEG Signals Through TSK Transfer Learning Fuzzy System.

Zhaoliang Zheng,Aiguo Chen,Jian Yao,Leyuan Zhou,Xuan Dong,Yang Ding

doi:10.3389/fnins.2021.738268

Zhaoliang Zheng, Aiguo Chen + Show 4 more

Open Access

https://doi.org/10.3389/fnins.2021.738268

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Abstract

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.

Highlights

Epilepsy is a disease caused by the sudden discharge of cerebral neurons
The problem of different data distribution has been solved to a certain extent, these transfer learning algorithms only consider reducing the marginal distribution probability or conditional distribution probability (Deng et al, 2018) of data, without comprehensive balance, and these algorithms lack of interpretability
We propose a new method of EEG recognition based on transfer learning and a fuzzy system

Summary

INTRODUCTION

Epilepsy is a disease caused by the sudden discharge of cerebral neurons. EEG technology (Suk et al, 2018) can monitor the changes of brain electrical signals, so we often use EEG intelligent recognition technology to detect epilepsy (Litt et al, 2001; Iasemidis et al, 2003; Dorai and Ponnambalam, 2010). The problem of different data distribution has been solved to a certain extent, these transfer learning algorithms only consider reducing the marginal distribution probability or conditional distribution probability (Deng et al, 2018) of data, without comprehensive balance, and these algorithms lack of interpretability To solve these problems, we propose a new method of EEG recognition based on transfer learning and a fuzzy system. In the section “Backgrounds,”we briefly introduced the EEG data set, the classical TSK model and the related contents of transfer learning. This section introduces the data sets and their processing methods used in the research, the classical TSK fuzzy system and the related content of transfer learning. Combined with the analysis and research on the rules and parameter learning strategies of the classical TSK fuzzy system, a TSK-TL method for detecting epileptic signals is proposed. Based on Pg, the final decision function can be obtained as follows

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Accuracy

EXPERIMENTAL PROCESS AND RESULTS ANALYSIS

Experimental Setup

CONCLUSION

DATA AVAILABILITY STATEMENT