ArticleIEEE transactions on bio-medical engineering2026
Bayesian Temporal Prediction: A Robust Algorithm for Real-Time EEG Phase-Dependent Brain Stimulation.
Article in IEEE transactions on bio-medical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Personalized, EEG-controlled intermittent theta burst stimulation.medRxiv : the preprint server for health sciences · 2026Article
- A structured narrative review on VNS-treated drug-resistant epilepsy: EEG markers, neurochemical mechanisms, and future biomarker-driven computational directions.Frontiers in neuroscience · 2026Review
- Personalized, EEG-controlled intermittent theta burst stimulation.Brain stimulationArticle
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Authors and funding
6 authors.
Funding
Abstract
objectiveReal-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.
methodsBTP 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.
resultsBTP demonstrates accurate EEG oscillation phase detection across a broad range of conditions and target oscillations, facilitating personalized brain stimulation.
conclusionThis 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.
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