Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods
Abstract
We assessed working memory load during computer use with neural network pattern recognition applied to EEG spectral features. Eight participants performed high-, moderate-, and low-load working memory tasks. Frontal theta EEG activity increased and alpha activity decreased with increasing load. These changes probably reflect task difficulty-related increases in mental effort and the proportion of cortical resources allocated to task performance. In network analyses, test data segments from high and low load levels were discriminated with better than 95% accuracy. More than 80% of test data segments associated with a moderate load could be discriminated from high- or low-load data segments. Statistically significant classification was also achieved when applying networks trained with data from one day to data from another day, when applying networks trained with data from one task to data from another task, and when applying networks trained with data from a group of participants to data from new participants. These results support the feasibility of using EEG-based methods for monitoring cognitive load during human-computer interaction.
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EEG has been shown to measure cognitive workload via changes in theta and alpha range signals.
“EEG has been shown to measure cognitive workload via changes in theta and alpha range signals (e.g.[18,17,16]).”
From Learn Piano with BACh: An Adaptive Learning Interface that Adjusts Task Difficulty Based on Brain State (2016) · cites this paper as [16]
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- Authors
- Gevins, A., Smith, M. E., Leong, H., McEvoy, L., Whitfield, S., Du, R., & Rush, G.
- Affiliations
- San Francicso Brain Research Institute & SAM Technology (US)
- SAM Technology and EEG Systems Laboratory, San Francisco, California
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 1998-03
- Volume
- 40
- Issue
- 1
- Pages
- 79-91
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W1998977696
- Semantic Scholar
- fd687094ae7be8cfb44a05d2a503e8ff9ccfe957
- Cited by
- 535
- References
- 2
- Citations by year
- 2026: 16 · 2025: 13 · 2024: 15 · 2023: 16 · 2022: 29 · 2021: 31 · 2020: 36 · 2019: 26 · 2018: 24 · 2017: 39 · 2016: 25 · 2015: 31 · 2014: 23 · 2013: 25 · 2012: 18
- FWCI
- 4.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Air Force
- Air Force Office of Scientific Research
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- working memory load, cognitive load monitoring, EEG, neural network pattern recognition, frontal theta activity, alpha activity, EEG spectral features, human-computer interaction, cross-subject generalization, cross-task generalization, cognitive effort
- MeSH terms
- Adult, Analysis of Variance, Brain Mapping, Female, Humans, Male, Memory, Short-Term, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, Reproducibility of Results, Workload, Neural Networks, Computer
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