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Information-Theoretic Approaches in EEG Correlates of Auditory Perceptual Awareness Under Informational Masking

Overview
Journal Biology (Basel)
Publisher MDPI
Specialty Biology
Date 2023 Jul 29
PMID 37508397
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Abstract

In informational masking paradigms, the successful segregation between the target and masker creates auditory perceptual awareness. The dynamics of the build-up of auditory perception is based on a set of interactions between bottom-up and top-down processes that generate neuronal modifications within the brain network activity. These neural changes are studied here using event-related potentials (ERPs), entropy, and integrated information, leading to several measures applied to electroencephalogram signals. The main findings show that the auditory perceptual awareness stimulated functional activation in the fronto-temporo-parietal brain network through (i) negative temporal and positive centro-parietal ERP components; (ii) an enhanced processing of multi-information in the temporal cortex; and (iii) an increase in informational content in the fronto-central cortex. These different results provide information-based experimental evidence about the functional activation of the fronto-temporo-parietal brain network during auditory perceptual awareness.

References
1.
Neff D, Green D . Masking produced by spectral uncertainty with multicomponent maskers. Percept Psychophys. 1987; 41(5):409-15. DOI: 10.3758/bf03203033. View

2.
Ferenets R, Vanluchene A, Lipping T, Heyse B, Struys M . Behavior of entropy/complexity measures of the electroencephalogram during propofol-induced sedation: dose-dependent effects of remifentanil. Anesthesiology. 2007; 106(4):696-706. DOI: 10.1097/01.anes.0000264790.07231.2d. View

3.
Fan J . An information theory account of cognitive control. Front Hum Neurosci. 2014; 8:680. PMC: 4151034. DOI: 10.3389/fnhum.2014.00680. View

4.
Kim H, Hudetz A, Lee J, Mashour G, Lee U . Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans. Front Hum Neurosci. 2018; 12:42. PMC: 5821001. DOI: 10.3389/fnhum.2018.00042. View

5.
Gramfort A, Luessi M, Larson E, Engemann D, Strohmeier D, Brodbeck C . MEG and EEG data analysis with MNE-Python. Front Neurosci. 2014; 7:267. PMC: 3872725. DOI: 10.3389/fnins.2013.00267. View