Hao Liu1,2,3 · Zihui Qi2,3 · Yihang Wang2 · Zhengyi Yang2 · Lingzhong Fan2 · Nianming Zuo2 · Tianzi Jiang1,2,3,41 School of Artifcial 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
Abstract
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.
Keywords
Real-time EEG phase prediction; Closedloop neuromodulation; EEG phase-triggered regulation; EEG rhythm; TMS-EEG co-registration
