EEG Recognition Method for Epileptic Patients Based on RNN Model with Attention Mechanism

被引：2

作者：

Zhou S. ^{[1
]}

Gao T.-H. ^{[1
]}

机构：

[1] School of Software, Northeastern University, Shenyang

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2023年 / 44卷 / 08期

关键词：

attention mechanism; EEG (electroencephalogram); epileptic patient; RNN (recurrent neural networks) model; XGBoost classifier;

D O I：

10.12068/j.issn.1005-3026.2023.08.005

中图分类号：

学科分类号：

摘要：

A RNN (recurrent neural networks) model based on attention mechanism is proposed for EEG (electroencephalogram) data recognition in epilepsy patients. Traditional EEG feature analysis is time-consuming and excessively dependent on expert experience, which greatly limits the application and popularization of brain activity recognition methods. A new EEG recognition method to solve the above problems is proposed. Firstly, the basic characteristics of EEG from epilepsy patients are analyzed. Then, the RNN model based on attention mechanism is designed to eliminate various interference signals and the XGBoost classifier is used to identify the categories of EEG data, so as to achieve the purpose of automatic refinement and recognition of the original EEG. Finally, a large number of experiments are carried out on the public EEG data set to verify the accuracy of the proposed method. The experimental results show that compared with the mature EEG recognition methods, the proposed method has higher recognition accuracy. © 2023 Northeastern University. All rights reserved.

引用

页码：1098 / 1103

页数：5

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