Paper Number
1202
Paper Type
Short Paper
Abstract
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.
Recommended Citation
Stano, Fabio and Knierim, Michael Thomas, "Towards EEG-Based Decision Support Systems: Externalizing Neural Information to Assist Economic Decisions" (2024). ECIS 2024 Proceedings. 5.
https://aisel.aisnet.org/ecis2024/track09_coghbis/track09_coghbis/5
Towards EEG-Based Decision Support Systems: Externalizing Neural Information to Assist Economic Decisions
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.
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