ArticleNeuroscience bulletin2025
A Novel Real-time Phase Prediction Network in EEG Rhythm.
Article in Neuroscience bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Delta-Band Dynamics of CaMKI-Positive Preoptic Neurons Encode Thermal Homeostatic Information.Neuroscience bulletin · 2026Article
- Aperiodic Brain Activity Modulates Corticospinal Excitability.Neuroscience bulletin · 2026Article
- Lightweight EEG Phase Prediction Based on Channel Attention and Spatio-Temporal Parallel Processing.Brain sciences · 2025Article
- Novel Strategies for Cognitive Enhancement via Noninvasive Neuromodulation.Neuroscience bulletin · 2025Article
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Authors and funding
7 authors.
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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.
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