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

In: Information Systems and Neuroscience

Author

Listed:
  • 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))

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.

Suggested Citation

  • Fabio Stano & Niels Doehring & Michael Thomas Knierim & Christof Weinhardt, 2025. "Single-Trial Economic Decision Classification with Passive BCIs: A Pilot Study," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 375-383, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-71385-9_33
    DOI: 10.1007/978-3-031-71385-9_33
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