Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data

Research output: Contribution to journalJournal articlepeer-review

Standard

Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data. / Levakova, Marie; Christensen, Jeppe Høy; Ditlevsen, Susanne.

In: Royal Society Open Science, Vol. 9, 220621, 11.2022.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Levakova, M, Christensen, JH & Ditlevsen, S 2022, 'Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data', Royal Society Open Science, vol. 9, 220621. https://doi.org/10.1098/rsos.220621

APA

Levakova, M., Christensen, J. H., & Ditlevsen, S. (2022). Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data. Royal Society Open Science, 9, [220621]. https://doi.org/10.1098/rsos.220621

Vancouver

Levakova M, Christensen JH, Ditlevsen S. Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data. Royal Society Open Science. 2022 Nov;9. 220621. https://doi.org/10.1098/rsos.220621

Author

Levakova, Marie ; Christensen, Jeppe Høy ; Ditlevsen, Susanne. / Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data. In: Royal Society Open Science. 2022 ; Vol. 9.

Bibtex

@article{b406ca8519b946a896a3226a53a645fe,
title = "Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data",
abstract = "Electroencephalogram (EEG) is a popular tool for studying brain activity. Numerous statistical techniques exist to enhance understanding of the complex dynamics underlying the EEG recordings. Inferring the functional network connectivity between EEG channels is of interest, and non-parametric inference methods are typically applied. We propose a fully parametric model-based approach via cointegration analysis. It not only estimates the network but also provides further insight through cointegration vectors, which characterize equilibrium states, and the corresponding loadings, which describe the mechanism of how the EEG dynamics is drawn to the equilibrium. We outline the estimation procedure in the context of EEG data, which faces specific challenges compared with the common econometric problems, for which cointegration analysis was originally conceived. In particular, the dimension is higher, typically around 64; there is usually access to repeated trials; and the data are artificially linearly dependent through the normalization done in EEG recordings. Finally, we illustrate the method on EEG data from a visual task experiment and show how brain states identified via cointegration analysis can be utilized in further investigations of determinants playing roles in sensory identifications.",
author = "Marie Levakova and Christensen, {Jeppe H{\o}y} and Susanne Ditlevsen",
year = "2022",
month = nov,
doi = "10.1098/rsos.220621",
language = "English",
volume = "9",
journal = "Royal Society Open Science",
issn = "2054-5703",
publisher = "TheRoyal Society Publishing",

}

RIS

TY - JOUR

T1 - Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data

AU - Levakova, Marie

AU - Christensen, Jeppe Høy

AU - Ditlevsen, Susanne

PY - 2022/11

Y1 - 2022/11

N2 - Electroencephalogram (EEG) is a popular tool for studying brain activity. Numerous statistical techniques exist to enhance understanding of the complex dynamics underlying the EEG recordings. Inferring the functional network connectivity between EEG channels is of interest, and non-parametric inference methods are typically applied. We propose a fully parametric model-based approach via cointegration analysis. It not only estimates the network but also provides further insight through cointegration vectors, which characterize equilibrium states, and the corresponding loadings, which describe the mechanism of how the EEG dynamics is drawn to the equilibrium. We outline the estimation procedure in the context of EEG data, which faces specific challenges compared with the common econometric problems, for which cointegration analysis was originally conceived. In particular, the dimension is higher, typically around 64; there is usually access to repeated trials; and the data are artificially linearly dependent through the normalization done in EEG recordings. Finally, we illustrate the method on EEG data from a visual task experiment and show how brain states identified via cointegration analysis can be utilized in further investigations of determinants playing roles in sensory identifications.

AB - Electroencephalogram (EEG) is a popular tool for studying brain activity. Numerous statistical techniques exist to enhance understanding of the complex dynamics underlying the EEG recordings. Inferring the functional network connectivity between EEG channels is of interest, and non-parametric inference methods are typically applied. We propose a fully parametric model-based approach via cointegration analysis. It not only estimates the network but also provides further insight through cointegration vectors, which characterize equilibrium states, and the corresponding loadings, which describe the mechanism of how the EEG dynamics is drawn to the equilibrium. We outline the estimation procedure in the context of EEG data, which faces specific challenges compared with the common econometric problems, for which cointegration analysis was originally conceived. In particular, the dimension is higher, typically around 64; there is usually access to repeated trials; and the data are artificially linearly dependent through the normalization done in EEG recordings. Finally, we illustrate the method on EEG data from a visual task experiment and show how brain states identified via cointegration analysis can be utilized in further investigations of determinants playing roles in sensory identifications.

UR - http://dx.doi.org/10.1098/rsos.220621

U2 - 10.1098/rsos.220621

DO - 10.1098/rsos.220621

M3 - Journal article

C2 - 36465674

VL - 9

JO - Royal Society Open Science

JF - Royal Society Open Science

SN - 2054-5703

M1 - 220621

ER -

ID: 329250077