A Novel Real‑time Phase Prediction Network in EEG Rhythm  

作　　者：Hao Liu Zihui Qi Yihang Wang Zhengyi Yang Lingzhong Fan Nianming Zuo Tianzi Jiang 

机构地区：[1]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China [2]Brainnetome Center,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China [3]University of Chinese Academy of Sciences,Beijing 100049,China [4]Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital,Yongzhou 425000,China

出　　处：《Neuroscience Bulletin》2025年第3期391-405,共15页神经科学通报(英文版)

基　　金：supported by the Key Collaborative Research Program of the Alliance of International Science Organizations(ANSO-CR-KP-2022-10);Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project(2021ZD0200200);Natural Science Foundation of China(82151307,82202253,and 31620103905);Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32030207);Science Frontier Program of the Chinese Academy of Sciences(QYZDJ-SSW-SMCO19).

摘　　要：Closed-loop neuromodulation,especially using the phase of the electroencephalography(EEG)rhythm to assess the real-time brain state and optimize the brain stimulation process,is becoming a hot research topic.Because the EEG signal is non-stationary,the commonly used EEG phase-based prediction methods have large variances,which may reduce the accuracy of the phase prediction.In this study,we proposed a machine learning-based EEG phase prediction network,which we call EEG phase prediction network(EPN),to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data.We verified the performance of EPN on pre-recorded data,simulated EEG data,and a real-time experiment.Compared with widely used state-of-the-art models(optimized multi-layer filter architecture,auto-regress,and educated temporal prediction),EPN achieved the lowest variance and the greatest accuracy.Thus,the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.

关 键 词：Real-time EEG phase prediction Closedloop neuromodulation EEG phase-triggered regulation EEG rhythm TMS-EEG co-registration 

分 类 号：R318[医药卫生—生物医学工程]