Single-Trial Economic Decision Classification with Passive BCIs: A Pilot Study

Fabio Stano (), Niels Doehring (), Michael Thomas Knierim () and Christof Weinhardt ()
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Fabio Stano: Karlsruhe Institute of Technology (KIT)
Niels Doehring: University of Bremen
Michael Thomas Knierim: Karlsruhe Institute of Technology (KIT)
Christof Weinhardt: Karlsruhe Institute of Technology (KIT)

A chapter in Information Systems and Neuroscience, 2025, pp 375-383 from Springer

Abstract: Abstract Decision support systems that evaluate user decisions have the potential to improve financial decision-making by alerting users to potentially disadvantageous choices. However, the feasibility of such systems, especially in complex decision-making scenarios, remains underexplored. This work in progress aims to investigate to what extend EEG-based decision support systems can be implemented using current technology. In a pilot study, we adapted the Iowa Gambling Task, a well-established decision-making paradigm, and collected 33-channel EEG data from three participants. As a proof of concept, we used a convolutional neural network (EEGNet) to classify positive and negative feedback, achieving subject-dependent binary classification accuracies ranging from 67 to 75%. These findings demonstrate the potential for developing and evaluating decision support systems that detect suboptimal decisions in real-world financial applications.

Keywords: Electroencephalography (EEG); Event-Related Potentials (ERPs); Iowa Gambling Task (IGT); Decision support systems; EEGNet; Feedback classification; Economic decision making (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-71385-9_33

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