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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.

Abstract Background Education and health are crucial topics for public policies as both largely determine the future wellbeing of the society. Currently, several studies recognize that physical activity (PA) benefits brain health in children. However, most of these studies have not been carried out in developing countries or lack the transference into the education field. The Cogni-Action Project is divided into two stages, a cross-sectional study and a crossover-randomized trial. The aim of the first part is to establish the associations of PA, sedentarism, and physical fitness with brain structure and function, cognitive performance and academic achievement in Chilean schoolchildren (10–13 years-old). The aim of the second part is to determinate the acute effects of three PA protocols on neuroelectric indices during a working memory and a reading task. Methods PA and sedentarism will be self-reported and objectively-assessed with accelerometers in a representative subsample, whilst physical fitness will be evaluated through the ALPHA fitness test battery. Brain structure and function will be assessed by magnetic resonance imaging (MRI) in a randomized subsample. Cognitive performance will be assessed through the NeuroCognitive Performance Test, and academic achievement by school grades. In the second part 32 adolescents (12–13 year-old) will be cross-over randomized to these condition (i) “Moderate-Intensity Continuous Training” (MICT), (ii) “Cooperative High-Intensity Interval Training” (C-HIIT), and (iii) Sedentary condition. Neuroelectric indices will be measures by electroencephalogram (EEG) and eye-tracking, working memory by n-back task and reading comprehension by a reading task. Discussion The main strength of this project is that, to our knowledge, this is the first study analysing the potential association of PA, sedentarism, and physical fitness on brain structure and function, cognitive performance, and academic achievement in a developing country, which presents an important sociocultural gap. For this purpose, this project will use advanced technologies in neuroimaging (MRI), electrophysiology (EEG), and eye-tracking, as well as objective and quality measurements of several physical and cognitive health outcomes. Trial registration ClinicalTrials.gov identifier: NCT03894241 Date of register: March 28, 2019. Retrospectively Registered.