A Novel Real-time Phase Prediction Network in EEG Rhythm.

Journal: Neuroscience bulletin
PMID: 39612043

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.

Authors

  • Hao Liu
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Zihui Qi
    Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
  • Yihang Wang
    Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, MD, 20742, USA.
  • Zhengyi Yang
    National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Lingzhong Fan
    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Nianming Zuo
    National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Tianzi Jiang
    Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 100190 Beijing, China; University of Chinese Academy of Sciences, 100049 Beijing, China.

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