Bayesian Temporal Prediction: A Robust Algorithm for Real-Time EEG Phase-Dependent Brain Stimulation

Objective: Real-time estimation of brain state is essential for efficient brain stimulation. Specifically, the electroencephalography (EEG) oscillation phase arose as a promising biomarker for instantaneous brain excitability, making it ideal for state-dependent brain stimulation. Current methods for real-time EEG phase extraction lose accuracy in the presence of non-stationary noise, motivating the development of a more robust and accurate algorithm. Here, we propose and validate Bayesian Temporal Prediction (BTP) as an effective method for EEG phase detection in real-time. Methods: BTP utilizes a short pre-session EEG recording and learning of the personalized prediction parameters, enabling subsequent high-precision real-time phase detection. We experimentally validate BTP in humans and compare its performance to a strong benchmark algorithm. Results: BTP demonstrates accurate EEG oscillation phase detection across a broad range of conditions and target oscillations, facilitating personalized brain stimulation. Conclusion: This study introduces BTP as a robust, computationally efficient, and accurate method for EEG state-dependent stimulation. Significance: The widespread adoption of BTP in research and clinical settings has the potential to enhance treatment efficacy and minimize inter- and intra-individual variability in brain stimulation interventions.