EEG phase prediction method based on long short-term memory network
10.19745/j.1003-8868.2025040
- VernacularTitle:基于长短期记忆网络的脑电相位预测方法研究
- Author:
Zi-yan PANG
1
;
Xin-yu ZHAO
;
Wen-shu MAI
;
Yue-zhuo ZHAO
;
Zhi-peng LIU
;
Tao YIN
;
Jing-na JIN
Author Information
1. 中国医学科学院北京协和医学院生物医学工程研究所,天津 300192;天津市神经调控与修复重点实验室,天津 300192
- Publication Type:Journal Article
- Keywords:
transcranial magnetic stimulation;
electroencephalogram;
electroencephalogram phase;
long short-term memory network;
autoregressive;
electroencephalogram signal
- From:
Chinese Medical Equipment Journal
2025;46(3):1-8
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]
