Temporally distinct oscillatory codes of retention and manipulation of verbal working memory

Pavlov, Yuri G.; Kotchoubey, Boris

doi:10.1101/2021.03.13.435253

Cited by 3 publications

(3 citation statements)

References 74 publications

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“…Raw data to define marginal distributions Brunyé et al, 2013;Giles et al, 2014;Mathiassen et al, 2014;Solis et al, 2015;Inoue et al, 2016;Harris et al, 2017;Hutchinson et al, 2017;Klein et al, 2017;Rosenbaum et al, 2017;Heuberger et al, 2018;Sandra and Otto, 2018;Schumacher et al, 2018;Angelidis et al, 2019;Bock et al, 2019;Goldfarb et al, 2019;Okano et al, 2019;Pyke et al, 2019;Sanabria et al, 2019;Wei et al, 2019;Barrett et al, 2020;Baumert et al, 2020;Fiedler et al, 2020;Holgado et al, 2020;Johnson et al, 2020;Knelange and López-Moliner, 2020;Larsen et al, 2020;Lin H. et al, 2020;Madore et al, 2020;Pahwa et al, 2020;Rodas and Greene, 2020;Rodeback et al, 2020;Timme and Brand, 2020;Tsukahara et al, 2020;Vine et al, 2020;von Helversen and Rieskamp, 2020;Pavlov and Kotchoubey, 2021 Effect size estimates to define xy relationships Lisper and Kjellberg, 1972;Glenville et al, 1978;Larsson, 1989;…”

Section: Data Derived Original Resourcesunclassified

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

et al. 2021

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Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

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Section: Data Derived Original Resourcesunclassified

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

et al. 2021

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“…With its high temporal resolution, EEG data can provide information 17 regarding the functional connectivity within the brain, thereby providing a topolog-18 ical understanding of the functioning of the human brain [3]. Usually carried out by 19 transforming the electrical potentials into a correlation matrix [4]. 20 To understand the functional aspects of the brain under conditions of executive Network analysis and Learning models on neuroimaging data have enabled re-27 searchers to study the human brain's functional and structural connectivity [5].…”

Section: Introduction 12mentioning

confidence: 99%

“…For the Probabilistic Selection Task [17] for each participant, one with a placebo and the other with the drug. Here the placebo is 102 treated as zero and drug administered session as one [19].…”

Section: Introduction 12mentioning

confidence: 99%

Application of Graph Theory Features towards EEG Data Classification Models for Working Memory and The Emotional States

Prakash¹,

G²,

Baths³

2021

Preprint

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Functional Connectivity analysis using Electroencephalography signals is a common 2 practice. The EEG signals are converted to networks by transforming the signals into a correlation 3 matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regres 4 sion, Random Forest, Support Vector Machine, and Recurrent Neural Networks, are implemented 5 on the correlation matrix data to classify them either on their psychometric assessment or the 6 effect of therapy. The classifications based on RNN provided higher accuracy( 74-88%) compared 7 to the other three models( 50-78%). The use of a correlation matrix, instead of using individual 8 graph features provides an initial test of the data. When compared with the time-resolved correlation 9 matrix it provided 4-5% higher accuracy.

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A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study

Prakash¹,

Baboo²,

Baths³

2021

BDCC

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Brain connectivity is studied as a functionally connected network using statistical methods such as measuring correlation or covariance. The non-invasive neuroimaging techniques such as Electroencephalography (EEG) signals are converted to networks by transforming the signals into a Correlation Matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Networks (RNN), are implemented on two different types of correlation matrices: Correlation Matrix (static connectivity) and Time-resolved Correlation Matrix (dynamic connectivity), to classify them either on their psychometric assessment or the effect of therapy. These correlation matrices are different from traditional learning techniques in the sense that they incorporate theory-based graph features into the learning models, thus providing novelty to this study. The EEG data used in this study is trail-based/event-related from five different experimental paradigms, of which can be broadly classified as working memory tasks and assessment of emotional states (depression, anxiety, and stress). The classifications based on RNN provided higher accuracy (74–88%) than the other three models (50–78%). Instead of using individual graph features, a Correlation Matrix provides an initial test of the data. When compared with the Time-resolved Correlation Matrix, it offered a 4–5% higher accuracy. The Time-resolved Correlation Matrix is better suited for dynamic studies here; it provides lower accuracy when compared to the Correlation Matrix, a static feature.

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Temporally distinct oscillatory codes of retention and manipulation of verbal working memory

Cited by 3 publications

References 74 publications

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Application of Graph Theory Features towards EEG Data Classification Models for Working Memory and The Emotional States

A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study

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