Bayesian Temporal Prediction: A Robust Algorithm for Real-time EEG Phase-dependent Brain Stimulation.

IF 4.5 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Sina Shirinpour, Ivan Alekseichuk, Malte R Guth, Zachary Haigh, Miles Wischnewski, Alexander Opitz
{"title":"Bayesian Temporal Prediction: A Robust Algorithm for Real-time EEG Phase-dependent Brain Stimulation.","authors":"Sina Shirinpour, Ivan Alekseichuk, Malte R Guth, Zachary Haigh, Miles Wischnewski, Alexander Opitz","doi":"10.1109/TBME.2025.3589970","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>BTP demonstrates accurate EEG oscillation phase detection across a broad range of conditions and target oscillations, facilitating personalized brain stimulation.</p><p><strong>Conclusion: </strong>This study introduces BTP as a robust, computationally efficient, and accurate method for EEG state-dependent stimulation.</p><p><strong>Significance: </strong>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.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5000,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/TBME.2025.3589970","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

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.

贝叶斯时间预测:实时脑电相位依赖脑刺激的鲁棒算法。
目的:脑状态的实时估计是有效的脑刺激的必要条件。具体来说,脑电图(EEG)振荡阶段作为一种有希望的瞬时脑兴奋性生物标志物出现,使其成为状态依赖性脑刺激的理想选择。当前的实时脑电信号相位提取方法在非平稳噪声的存在下失去了准确性,这促使开发更鲁棒和更准确的算法。在此,我们提出并验证了贝叶斯时间预测(BTP)作为实时EEG相位检测的有效方法。方法:BTP利用短暂的会前脑电图记录和个性化预测参数的学习,实现随后的高精度实时相位检测。我们通过实验验证了人类的BTP,并将其性能与强大的基准算法进行了比较。结果:BTP在广泛的条件和目标振荡中显示了准确的脑电图振荡相位检测,促进了个性化的脑刺激。结论:本研究将BTP作为一种鲁棒性、计算效率高且准确的EEG状态依赖性刺激方法。意义:在研究和临床环境中广泛采用BTP有可能提高治疗效果,并最大限度地减少脑刺激干预的个体间和个体内差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Transactions on Biomedical Engineering
IEEE Transactions on Biomedical Engineering 工程技术-工程:生物医学
CiteScore
9.40
自引率
4.30%
发文量
880
审稿时长
2.5 months
期刊介绍: IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信