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

doi:10.12068/j.issn.1005-3026.2023.08.005

Abstract

Abstract: 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.

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

CLC Number:

TP391

ZHOU Song， GAO Tian-han. EEG Recognition Method for Epileptic Patients Based on RNN Model with Attention Mechanism[J]. Journal of Northeastern University(Natural Science), 2023, 44(8): 1098-1103.

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