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Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks

Authors:

Nargess Nourbakhsh,

Rafael A. CalvoAuthors Info & Claims

OzCHI '12: Proceedings of the 24th Australian Computer-Human Interaction Conference

Pages 420 - 423

https://doi.org/10.1145/2414536.2414602

Published: 26 November 2012 Publication History

Abstract

Galvanic Skin Response (GSR) has recently attracted researchers' attention as a prospective physiological indicator of cognitive load and emotions. However, it has commonly been investigated through single or few measures and in one experimental scenario. In this research, aiming to perform a comprehensive study, we have assessed GSR data captured from two different experiments, one including text reading tasks and the other using arithmetic tasks, each imposing multiple cognitive load levels. We have examined temporal and spectral features of GSR against different task difficulty levels. ANOVA test was applied for the statistical evaluation. Obtained results show the strong significance of the explored features, especially the spectral ones, in cognitive workload measurement in the two studied experiments.

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Cited By

Page CLiu CMeltzer JGhosh Hajra S(2024)Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking EnvironmentsSensors10.3390/s2404108224:4(1082)Online publication date: 7-Feb-2024
https://doi.org/10.3390/s24041082
Lennon-Maslin MQuaiser-Pohl C(2024)“It’s Different for Girls!” The Role of Anxiety, Physiological Arousal, and Subject Preferences in Primary School Children’s Math and Mental Rotation PerformanceBehavioral Sciences10.3390/bs1409080914:9(809)Online publication date: 12-Sep-2024
https://doi.org/10.3390/bs14090809
Sazuka NKatsumata KKomoriya YOba TOhira 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 learningFrontiers in Human Neuroscience10.3389/fnhum.2024.127212118Online publication date: 29-Feb-2024
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Index Terms

Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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OzCHI '12: Proceedings of the 24th Australian Computer-Human Interaction Conference

November 2012

692 pages

ISBN:9781450314381

DOI:10.1145/2414536

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New Zealand Chapter of ACM SIGCHI
Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2012

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OzCHI '12

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Human Factors & Ergonomics Soc

OzCHI '12: The 24th Australian Computer-Human Interaction Conference

November 26 - 30, 2012

Melbourne, Australia

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Page CLiu CMeltzer JGhosh Hajra S(2024)Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking EnvironmentsSensors10.3390/s2404108224:4(1082)Online publication date: 7-Feb-2024
https://doi.org/10.3390/s24041082
Lennon-Maslin MQuaiser-Pohl C(2024)“It’s Different for Girls!” The Role of Anxiety, Physiological Arousal, and Subject Preferences in Primary School Children’s Math and Mental Rotation PerformanceBehavioral Sciences10.3390/bs1409080914:9(809)Online publication date: 12-Sep-2024
https://doi.org/10.3390/bs14090809
Sazuka NKatsumata KKomoriya YOba TOhira 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 learningFrontiers in Human Neuroscience10.3389/fnhum.2024.127212118Online publication date: 29-Feb-2024
https://doi.org/10.3389/fnhum.2024.1272121
Uğuz Arsu Ş(2024)YÖNETİMDE YENİ BİR KAVRAM OLARAK NÖROYÖNETİM: DENEYSEL ÇALIŞMALARIN SİSTEMATİK OLARAK İNCELENMESİPamukkale University Journal of Social Sciences Institute10.30794/pausbed.1398796Online publication date: 22-Mar-2024
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Vanheusden FOgilvie M(2024)Objective evaluation of seat discomfort on eRacing performanceWORK: A Journal of Prevention, Assessment & Rehabilitation10.1177/10519815241290330Online publication date: 4-Dec-2024
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Wang ZOuyang YZeng H(2024)ARFN: An Attention-Based Recurrent Fuzzy Network for EEG Mental Workload AssessmentIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.336914373(1-14)Online publication date: 2024
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Li ZQiao KZou Z(2024)A Measurement System for Assessing Interaction Levels with Intelligent Chatbots: The Flow State Measurement SystemIEEE Instrumentation & Measurement Magazine10.1109/MIM.2024.1074313427:8(23-29)Online publication date: Nov-2024
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Wang JWang AHu HWu KHe D(2024)Multi-Source Domain Generalization for ECG-Based Cognitive Load Estimation: Adversarial Invariant and Plausible Uncertainty LearningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447676(1631-1635)Online publication date: 14-Apr-2024
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Larraga-García BRuiz Bejerano VBlanco PNavarro SÁlvarez MOregui XAzpiroz IChatzigeorgiou CGarcía Olaizola IÁlvarez FGutiérrez Á(2024)Effect of Multitasking on Cognitive Functions, Performance, and Bio-Signals in First RespondersIEEE Access10.1109/ACCESS.2024.350806612(178104-178116)Online publication date: 2024
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Yang PLiu NLiu XShu YJi WRen ZSheng JYu MYi RZhang DLiu Y(2024)A Multimodal Dataset for Mixed Emotion RecognitionScientific Data10.1038/s41597-024-03676-411:1Online publication date: 5-Aug-2024
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