Association of brain–autonomic activities and task accuracy under cognitive load - a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learning

Aug 5, 2026

Cognitive load reflects the mental resources applied to working memory at a given time and critically shapes performance in learning and daily activities. This pilot study links brain activity to autonomic function and behavior by introducing EEG-based feature quantities capturing time-series power changes across frequency bands, while concurrently measuring HRV and spontaneous skin conductance responses. A previously developed machine-learning model estimates arousal level from EEG to interpret brain–autonomic–behavior relationships under cognitive load. In 12 healthy undergraduates, higher infraslow alpha power fluctuations relate to more efficient information processing, HRV parasympathetic indices associate with task accuracy, and machine-learning arousal estimation shows a robust EEG relationship, supporting task-performance prediction under cognitive load.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Brain activity and cognitive load

Behavioral measures and individual differences

EEG and autonomic measures

TYPE Brief Research Report PUBLISHED 29 February 2024 DOI 10. 3389/fnhum.2024.1272121 OPEN ACCESS EDITED BY Claudio Lucchiari, University of Milan, Italy REVIEWED BY Grzegorz Marcin Wójcik, Marie Curie-Sklodowska University, Poland Maria Elide Vanutelli, University of Milano-Bicocca, Italy *CORRESPONDENCE Naoya Sazuka [naoya.sazuka@sony.com](naoya.sazuka@sony.com) RECEIVED 15 August 2023 ACCEPTED 09 February 2024 PUBLISHED 29 February 2024 CITATION Sazuka N, Katsumata K, Komoriya Y, Oba T and Ohira H (2024) Association of brain–autonomic activities and task accuracy under cognitive load: a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learning. Front. Hum. Neurosci. 18:1272121 . doi: 10.3389/fnhum.2024.1272121 COPYRIGHT © 2024 Sazuka, Katsumata, Komoriya, Oba and Ohira. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Association of brain–autonomic activities and task accuracy under cognitive load: a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learning Naoya Sazuka1*, Koki Katsumata1 , Yota Komoriya1 , Takeyuki Oba2 and Hideki Ohira2 1 Human Technology Research and Development Department, Application Technology Research and Development Division, Technology Development Laboratories, Sony Corporation, Tokyo, Japan, 2 Department of Cognitive and Psychological Sciences, School of Informatics, Nagoya University, Nagoya, Japan The total amount of mental activity applied to working memory at a given point in time is called cognitive load, which is an important factor in various activities in daily life. We have proposed new feature quantities that reﬂect the time-series changes in the power of typical frequency bands in electroencephalogram (EEG) for use in examining the relationship between brain activity and behavior under cognitive load. We also measured heart rate variability (HRV) and spontaneous skin conductance responses (SCR) to examine functional associations among brain activity, autonomic activity, and behavior under cognitive load. Additionally, we applied our machine learning model previously developed using EEG to the estimation of arousal level to interpret the brain–autonomic–behavior functional association under cognitive load. Experimental data from 12 healthy undergraduate students showed that participants with higher levels of infraslow ﬂuctuations of alpha power have more cognitive resources and thus can process information under cognitive load more e􀀈ciently. In addition, HRVreﬂecting parasympathetic activity correlated with task accuracy. The arousal level estimated using our machine learning model showed its robust relationship with EEG. Despite the limitation of the sample size, the results of this pilot study suggest that the information processing e􀀈ciency of the brain under cognitive load is reﬂected by time-series ﬂuctuations in EEG, which are associated with an individual’s task performance. These ﬁndings can contribute to the evaluation of the internal state of humans associated with cognitive load and the prediction of human behaviors in various situations under cognitive load. KEYWORDS cognitive load, electroencephalogram (EEG), heart rate variability, skin conductance response, machine learning, infra-slow ﬂuctuations of alpha power Frontiers inHuman Neuroscience 01 [frontiersin.org](frontiersin.org) 1 Introduction The total amount of mental activity applied to working memory at a given point in time is called cogni

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What key findings were observed with respect to EEG fluctuations and performance?

Participants with higher infraslow fluctuations of alpha power showed more cognitive resources and processed information more efficiently under cognitive load; HRV reflecting parasympathetic activity correlated with task accuracy, and machine-learning arousal estimation showed a robust relationship with EEG.

Aug 5, 2026

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