research-article

Combining EEG with Pupillometry to Improve Cognitive Workload Detection

Authors:

David Rozado,

Andreas DunserAuthors Info & Claims

Computer, Volume 48, Issue 10

Pages 18 - 25

https://doi.org/10.1109/MC.2015.314

Published: 01 October 2015 Publication History

Abstract

Combining signals from traditional electroencephalography with those of novel physiological measures such as pupil dilation could enable more accurate and robust real-time monitoring of cognitive workload.

References

[1]

K. Ryu and R. Myung, “Evaluation of Mental Workload with a Combined Measure Based on Physiological Indices during a Dual Task of Tracking and Mental Arithmetic,” Int'l J. Industrial Ergonomics, vol. 35, no. 11, 2005, pp. 991–1009.

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[2]

T. Fritz et al., “Using Psycho-physiological Measures to Assess Task Difficulty in Software Development,” Proc. 36th Int'l Conf. Software Eng. (ICSE 14), 2014 pp. 402–413.

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[3]

E. Haapalainen et al., “Psycho-physiological Measures for Assessing Cognitive Workload,” Proc. 12th ACM Int'l Conf. Ubiquitous Computing (UbiComp 10), 2010, pp. 301–310.

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Cited By

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Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582272
Aygun ALyu BNguyen THaga ZAeron SScheutz M(2022)Cognitive Workload Assessment via Eye Gaze and EEG in an Interactive Multi-Modal Driving TaskProceedings of the 2022 International Conference on Multimodal Interaction10.1145/3536221.3556610(337-348)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3536221.3556610
Gupta KHajika RPai YDuenser ALochner MBillinghurst M(2019)In AI We Trust: Investigating the Relationship between Biosignals, Trust and Cognitive Load in VRProceedings of the 25th ACM Symposium on Virtual Reality Software and Technology10.1145/3359996.3364276(1-10)Online publication date: 12-Nov-2019
https://dl.acm.org/doi/10.1145/3359996.3364276

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Combining EEG with Pupillometry to Improve Cognitive Workload Detection

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Published In

Computer Volume 48, Issue 10

Oct. 2015

100 pages

ISSN:0018-9162

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 October 2015

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Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582272
Aygun ALyu BNguyen THaga ZAeron SScheutz M(2022)Cognitive Workload Assessment via Eye Gaze and EEG in an Interactive Multi-Modal Driving TaskProceedings of the 2022 International Conference on Multimodal Interaction10.1145/3536221.3556610(337-348)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3536221.3556610
Gupta KHajika RPai YDuenser ALochner MBillinghurst M(2019)In AI We Trust: Investigating the Relationship between Biosignals, Trust and Cognitive Load in VRProceedings of the 25th ACM Symposium on Virtual Reality Software and Technology10.1145/3359996.3364276(1-10)Online publication date: 12-Nov-2019
https://dl.acm.org/doi/10.1145/3359996.3364276

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Recommendations

EOG metrics for cognitive workload detection

Detection of vigilance performance with pupillometry

Cognitive Workload Assessment via Eye Gaze and EEG in an Interactive Multi-Modal Driving Task

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