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Rest Assured: Detecting Mental Fatigue and Recovery with EEG Headphones

Schick, Lukas ORCID iD icon 1; Frey, Emilia 2; Putze, Felix; Knierim, Michael T. 1
1 Institut für Wirtschaftsinformatik (WIN), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)

Abstract:

Mental fatigue, a common consequence of cognitively demanding work, impairs concentration and well-being, posing long-term health risks. Distinct from drowsiness, mental fatigue is reliably measured with EEG, yet conventional setups remain too cumbersome for everyday use. To overcome this barrier, this study investigates whether EEG headphones can detect mental fatigue and recovery across two common digital break activities: playing a video game and browsing social media. We conducted an experiment with consecutive task sessions and an intermittent break, collecting selfreport, performance, and EEG data. Our results show that EEG headphones can detect mental fatigue and recovery dynamics via relative alpha power, and differentiate recovery effects between break types. Social media proved more restorative than gaming, with effects persisting into the subsequent task. These findings establish needed working principles for using headphone-EEG in naturalistic fatigue and recovery research, providing a foundation for future studies.


Verlagsausgabe §
DOI: 10.5445/IR/1000193761
Veröffentlicht am 03.06.2026
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik (WIN)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 13.04.2026
Sprache Englisch
Identifikator ISBN: 979-8-4007-2278-3
KITopen-ID: 1000193761
Erschienen in Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, BArcelona, 13th-17th April 2026
Veranstaltung Conference on Human Factors in Computing Systems (CHI 2026), Barcelona, Spanien, 13.04.2026 – 17.04.2026
Verlag Association for Computing Machinery (ACM)
Seiten 1–17
Nachgewiesen in OpenAlex
Scopus
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