Computer Science > Human-Computer Interaction
arXiv:2305.08044 (cs)
[Submitted on 14 May 2023]
Title:Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario
View a PDF of the paper titled Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario, by Kuan-Jung Chiang and 3 other authors
View PDFAbstract:Objective: The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states. This study investigated the associations between memory workload and EEG during participants' typical office tasks on a single-monitor and dual-monitor arrangement. We expect a higher memory workload for the single-monitor arrangement. Approach: We designed an experiment that mimics the scenario of a subject performing some office work and examined whether the subjects experienced various levels of memory workload in two different office setups: 1) a single-monitor setup and 2) a dual-monitor setup. We used EEG band power, mutual information, and coherence as features to train machine learning models to classify high versus low memory workload states. Main results: The study results showed that these characteristics exhibited significant differences that were consistent across all participants. We also verified the robustness and consistency of these EEG signatures in a different data set collected during a Sternberg task in a prior study. Significance: The study found the EEG correlates of memory workload across individuals, demonstrating the effectiveness of using EEG analysis in conducting real-world neuroergonomic studies.
| Comments: | This paper is published in Journal of Neural Engineering (2023). 19 pages, 9 figures |
| Subjects: | Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2305.08044 [cs.HC] |
| (orarXiv:2305.08044v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2305.08044 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1088/1741-2552/accbed DOI(s) linking to related resources |
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View a PDF of the paper titled Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario, by Kuan-Jung Chiang and 3 other authors
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