CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue
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 Wisconsi...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-06-01
|
| Series: | Technologies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7080/7/2/46 |
| _version_ | 1818135742267785216 |
|---|---|
| author | Michalis Papakostas Akilesh Rajavenkatanarayanan Fillia Makedon |
| author_facet | Michalis Papakostas Akilesh Rajavenkatanarayanan Fillia Makedon |
| author_sort | Michalis Papakostas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T09:29:21Z |
| format | Article |
| id | doaj.art-1dcf18765e8542ceb9b12a070a86db64 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-12-11T09:29:21Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-1dcf18765e8542ceb9b12a070a86db642022-12-22T01:13:03ZengMDPI AGTechnologies2227-70802019-06-01724610.3390/technologies7020046technologies7020046CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive FatigueMichalis Papakostas0Akilesh Rajavenkatanarayanan1Fillia Makedon2The Heracleia Human Centered Computing Laboratory, Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USAThe Heracleia Human Centered Computing Laboratory, Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USAThe Heracleia Human Centered Computing Laboratory, Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USAIn 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.https://www.mdpi.com/2227-7080/7/2/46behavioral and cognitive modelingmulti-modal datasetuser modeling and monitoringcognitive fatigueadaptive interactionuser monitoringcognitive assessmentEEGmachine learning |
| spellingShingle | Michalis Papakostas Akilesh Rajavenkatanarayanan Fillia Makedon CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue Technologies behavioral and cognitive modeling multi-modal dataset user modeling and monitoring cognitive fatigue adaptive interaction user monitoring cognitive assessment EEG machine learning |
| title | CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue |
| title_full | CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue |
| title_fullStr | CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue |
| title_full_unstemmed | CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue |
| title_short | CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue |
| title_sort | cogbeacon a multi modal dataset and data collection platform for modeling cognitive fatigue |
| topic | behavioral and cognitive modeling multi-modal dataset user modeling and monitoring cognitive fatigue adaptive interaction user monitoring cognitive assessment EEG machine learning |
| url | https://www.mdpi.com/2227-7080/7/2/46 |
| work_keys_str_mv | AT michalispapakostas cogbeaconamultimodaldatasetanddatacollectionplatformformodelingcognitivefatigue AT akileshrajavenkatanarayanan cogbeaconamultimodaldatasetanddatacollectionplatformformodelingcognitivefatigue AT filliamakedon cogbeaconamultimodaldatasetanddatacollectionplatformformodelingcognitivefatigue |