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Towards EEG-Based Decision Support Systems: Externalizing Neural Information to Assist Economic Decisions

Stano, Fabio ORCID iD icon 1; Knierim, Michael Thomas 1
1 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Making informed decisions in an era of vast choice options is increasingly presenting as a problem. In an effort to overcome task-specific decision support systems (DSS), this article proposes a novel, NeuroIS-based approach that leverages Electroencephalography (EEG) feedback to enhance decision-making in complex scenarios - in this case where individuals must decide between multiple concurrent investment options. Our research comes with a two-fold aim, to initially assess the feasibility of using a well-known EEG feature – decision preceding negativity (DPN) – as a predictor of disadvantageous choices. Afterwards, we can investigate how a novel, task-independent and user-centric DSS, could influence decision processes and quality by intervening in critical decision-making moments. In this article, we present the experiment design featured in both stages and highlight our expected theoretical and practical contributions.


Zugehörige Institution(en) am KIT Fakultät für Wirtschaftswissenschaften – Institut für Informationswirtschaft und -management (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 06.2024
Sprache Englisch
Identifikator KITopen-ID: 1000187260
Erschienen in European Conference on Information Systems
Veranstaltung 32nd European Conference on Information Systems (ECIS 2024), Paphos, Zypern, 13.06.2024 – 19.06.2024
Verlag AIS Electronic Library (AISeL)
Seiten 1-8
Serie ECIS 2024 Proceedings
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