Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data
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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 journal › Journal article › Research › peer-review
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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