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Open AccessArticle

CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue

The Heracleia Human Centered Computing Laboratory, Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
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Technologies 2019, 7(2), 46; https://doi.org/10.3390/technologies7020046
Received: 2 April 2019 / Revised: 23 May 2019 / Accepted: 12 June 2019 / Published: 13 June 2019
(This article belongs to the Special Issue Multimedia and Cross-modal Retrieval)
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Abstract

In this work, we present CogBeacon, a multi-modal dataset designed to target the effects of cognitive fatigue in human performance. The dataset consists of 76 sessions collected from 19 male and female users performing different versions of a cognitive task inspired by the principles of the Wisconsin Card Sorting Test (WCST), a popular cognitive test in experimental and clinical psychology designed to assess cognitive flexibility, reasoning, and specific aspects of cognitive functioning. During each session, we record and fully annotate user EEG functionality, facial keypoints, real-time self-reports on cognitive fatigue, as well as detailed information of the performance metrics achieved during the cognitive task (success rate, response time, number of errors, etc.). Along with the dataset we provide free access to the CogBeacon data-collection software to provide a standardized mechanism to the community for collecting and annotating physiological and behavioral data for cognitive fatigue analysis. Our goal is to provide other researchers with the tools to expand or modify the functionalities of the CogBeacon data-collection framework in a hardware-independent way. As a proof of concept we show some preliminary machine learning-based experiments on cognitive fatigue detection using the EEG information and the subjective user reports as ground truth. Our experiments highlight the meaningfulness of the current dataset, and encourage our efforts towards expanding the CogBeacon platform. To our knowledge, this is the first multi-modal dataset specifically designed to assess cognitive fatigue and the only free software available to allow experiment reproducibility for multi-modal cognitive fatigue analysis. View Full-Text
Keywords: behavioral and cognitive modeling; multi-modal dataset; user modeling and monitoring; cognitive fatigue; adaptive interaction; user monitoring; cognitive assessment; EEG; machine learning behavioral and cognitive modeling; multi-modal dataset; user modeling and monitoring; cognitive fatigue; adaptive interaction; user monitoring; cognitive assessment; EEG; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Papakostas, M.; Rajavenkatanarayanan, A.; Makedon, F. CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue. Technologies 2019, 7, 46.

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