Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2024-01-15 DOI:10.1109/ACCESS.2024.3354711
Nelson J. Trujillo-Barreto;David Araya Galvez;Aland Astudillo;Wael El-Deredy
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Abstract

We consider the detection and characterization of brain state transitions based on ongoing electroencephalography (EEG). Here, a brain state represents a specific brain dynamical regime or mode of operation that produces a characteristic quasi-stable pattern of activity at the topography, sources, or network levels. These states and their transitions over time can reflect fundamental computational properties of the brain, shaping human behavior and brain function. The hidden Markov model (HMM) has emerged as a useful tool for uncovering the hidden dynamics of brain state transitions based on observed data. However, the limitations of the Geometric distribution of states’ durations (dwell times) implicit in the standard HMM, make it sub-optimal for modeling brain states in EEG. We propose using hidden semi Markov models (HSMM), a generalization of HMM that allows modeling the brain states duration distributions explicitly. We present a Bayesian formulation of HSMM and use the variational Bayes framework to efficiently estimate the HSMM parameters, the number of brain states, and select among candidate brain state duration distributions. We assess HSMM performance against HMM on simulated data and demonstrate that the accurate modeling of state durations is paramount for making reliable inference when the task at hand requires accurate model predictions. Finally, we use actual resting-state EEG data to illustrate the benefits of the approach in practice. We demonstrate that the possibility of modeling brain state durations explicitly provides a new way for investigating the nature of the neural dynamics that generated the EEG data.
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利用脑电图数据中的隐式半马尔可夫模型对大脑状态持续时间进行显式建模
我们考虑基于持续脑电图(EEG)对大脑状态转换进行检测和表征。在这里,大脑状态代表了一种特定的大脑动态机制或运行模式,它能在地形、源或网络水平上产生特征性的准稳定活动模式。这些状态及其随时间的转变可以反映大脑的基本计算特性,从而塑造人类行为和大脑功能。隐马尔可夫模型(HMM)已成为基于观测数据揭示大脑状态转换隐藏动态的有用工具。然而,标准 HMM 中隐含的状态持续时间(停留时间)几何分布的局限性,使其成为脑电图中大脑状态建模的次优选择。我们建议使用隐式半马尔可夫模型(HSMM),它是 HMM 的一种广义,可以明确地对大脑状态的持续时间分布进行建模。我们提出了 HSMM 的贝叶斯公式,并使用变异贝叶斯框架来有效估计 HSMM 参数、脑状态数量,并在候选脑状态持续时间分布中进行选择。我们在模拟数据上评估了 HSMM 与 HMM 的性能,并证明当手头的任务需要准确的模型预测时,状态持续时间的准确建模对于做出可靠的推断至关重要。最后,我们使用实际的静息态脑电数据来说明该方法在实践中的优势。我们证明,对大脑状态持续时间进行明确建模的可能性为研究产生脑电图数据的神经动力学性质提供了一种新方法。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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