Dataset Information

Using Data Assimilation for Quantitative Electroencephalography Analysis.

ABSTRACT: We propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of data assimilation (DA) in areas such as geosciences, meteorology, and aerospace. However, the use of this approach is less common in neurosciences. In our case, EnKF highlights the spectral contribution of brain signals that are more likely (according to their coherence analysis) to be related to the cognitive process of interest. The power enhancement, due to the cognitive activity, is later validated in the power spectrum analysis by comparing through statistical tests relevant frequency content in two datasets in which assessing the development of cognitive abilities is of interest: the process of getting concentrated and of learning a new skill. Our results show that our DA-based methodology can highlight important frequency characteristics of the electroencephalogram (EEG) data that have been related to different cognitive processes. Hence, our proposal has the potential to understand of neurocognitive phenomena that is tracked through QEEG.

SUBMITTER: Peralta-Malvaez L

PROVIDER: S-EPMC7696777 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Using Data Assimilation for Quantitative Electroencephalography Analysis.

Peralta-Malváez Lizbeth L Salazar-Varas Rocio R Etcheverry Gibran G Gutiérrez David D

Brain sciences 20201112 11

We propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of data assimilation (DA) in areas such as geosciences, meteorology, and aerospace. However, the use of this approach is less common in neurosciences. In our case, EnKF highlights the spectral contribution ...[more]

PMID: 33198422

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Project description:Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.

Dataset Information

Using Data Assimilation for Quantitative Electroencephalography Analysis.

Publications

Using Data Assimilation for Quantitative Electroencephalography Analysis.

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