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  • P-ISSN1013-0799
  • E-ISSN2586-2073
  • KCI

ISSN : 1013-0799

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Vol.41 No.1

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Digital Library Interface Research Based on EEG, Eye-Tracking, and Artificial Intelligence Technologies: Focusing on the Utilization of Implicit Relevance Feedback

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2024, v.41 no.1, pp.261-282
https://doi.org/10.3743/KOSIM.2024.41.1.261
Hyun-Hee Kim (Myongji University)
Yong-Ho Kim (Pukyong National University)
Kim, H., & Kim, Y. (2024). Digital Library Interface Research Based on EEG, Eye-Tracking, and Artificial Intelligence Technologies: Focusing on the Utilization of Implicit Relevance Feedback. Journal of the Korean Society for Information Management, 41(1), 261-282, https://doi.org/10.3743/KOSIM.2024.41.1.261

Abstract

This study proposed and evaluated electroencephalography (EEG)-based and eye-tracking-based methods to determine relevance by utilizing users’ implicit relevance feedback while navigating content in a digital library. For this, EEG/eye-tracking experiments were conducted on 32 participants using video, image, and text data. To assess the usefulness of the proposed methods, deep learning-based artificial intelligence (AI) techniques were used as a competitive benchmark. The evaluation results showed that EEG component-based methods (av_P600 and f_P3b components) demonstrated high classification accuracy in selecting relevant videos and images (faces/emotions). In contrast, AI-based methods, specifically object recognition and natural language processing, showed high classification accuracy for selecting images (objects) and texts (newspaper articles). Finally, guidelines for implementing a digital library interface based on EEG, eye-tracking, and artificial intelligence technologies have been proposed. Specifically, a system model based on implicit relevance feedback has been presented. Moreover, to enhance classification accuracy, methods suitable for each media type have been suggested, including EEG-based, eye-tracking-based, and AI-based approaches.

keywords
EEG, eye tracking, artificial intelligence, implicit relevance feedback, digital library interfaces
Submission Date
2024-02-15
Revised Date
2024-02-29
Accepted Date
2024-03-04

Journal of the Korean Society for Information Management

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