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.