KIT | KIT-Bibliothek | Impressum | Datenschutz

Could we Predict Flow from Ear-EEG?

Knierim, Michael Thomas 1; Bartholomeyczik, Karen ORCID iD icon 1; Nieken, Petra ORCID iD icon 2; Weinhardt, Christof ORCID iD icon 1
1 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)
2 Institut für Unternehmungsführung (IBU), Karlsruher Institut für Technologie (KIT)

Abstract:

Advancements in wearable EEG could provide valuable foundations for studying flow experiences in everyday life. In this study, we report initial findings on using unobtrusive, comfortable around-the-ear EEG electrodes (cEEGrids) to monitor flow levels. Tree-based regression models show that flow reports across three different tasks can be predicted with a mean absolute error (MAE) of 11% across study participants. These results represent a potential starting point for further research with cEEGrids on the momentary capturing of flow in everyday life. Related limitations and propositions are discussed.


Originalveröffentlichung
DOI: 10.1109/ACIIW57231.2022.10086037
Scopus
Zitationen: 1
Dimensions
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Unternehmungsführung (IBU)
Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2023
Sprache Englisch
Identifikator ISBN: 978-1-6654-5490-2
KITopen-ID: 1000151016
Erschienen in 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
Veranstaltung 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW 2022), Nara, Japan, 17.10.2022 – 21.10.2022
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 1-6
Vorab online veröffentlicht am 05.04.2023
Schlagwörter Flow experience, Mental workload, Ear EEG, cEEGrids, Machine Learning, XGBoost
Nachgewiesen in Dimensions
Scopus
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page