Brain-machine interfaces & information retrieval challenges and opportunities

Tools

Moshfeghi, Yashar and McGuire, Niall; (2025) Brain-machine interfaces & information retrieval challenges and opportunities. In: SIGIR '25. Association for Computing Machinery, ITA. (In Press)

[thumbnail of Moshfeghi-McGuire-ACM-SIGIR-2025-Brain-machine-interfaces-and-information-retrievalX] Text. Filename: Moshfeghi-McGuire-ACM-SIGIR-2025-Brain-machine-interfaces-and-information-retrievalX.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 1 January 2099.
Download (1MB) | Request a copy

Abstract

The fundamental goal of Information Retrieval (IR) systems lies in their capacity to effectively satisfy human information needs - a challenge that encompasses not just the technical delivery of information, but the nuanced understanding of human cognition during information seeking. Contemporary IR platforms rely primarily on observable interaction signals, creating a fundamental gap between system capabilities and users’ cognitive processes. Brain-Machine Interface (BMI) technologies now offer unprecedented potential to bridge this gap through direct measurement of previously inaccessible aspects of information-seeking behaviour. This perspective paper offers a broad examination of the IR landscape, providing a comprehensive analysis of how BMI technology could transform IR systems, drawing from advances at the intersection of both neuroscience and IR research. We present our analysis through three identified fundamental vertices: (1) understanding the neural correlates of core IR concepts to advance theoretical models of search behaviour, (2) enhancing existing IR systems through contextual integration of neurophysiological signals, and (3) developing proactive IR capabilities through direct neurophysiological measurement.

ORCID iDs

Moshfeghi, Yashar ORCID logoORCID: https://orcid.org/0000-0003-4186-1088 and McGuire, Niall;
CORE (COnnecting REpositories)