ArticleBiomedical engineering online2022
Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications.
Article in Biomedical engineering online, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- The application of fNIRS in studies on occupational workload: a systematic review.Frontiers in public health · 2025Pooled it
- Sensory processing sensitivity predicts pilot decision time via indecisiveness: evidence from flight simulation and fNIRS.BMC psychology · 2026Article
- Research on drug addiction detection based on AR-TSNET with bimodal EEG-NIRS.Biomedical engineering online · 2025Article
- Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments.Brain and behavior · 2025Article
- Exploring cognitive load through neuropsychological features: an analysis using fNIRS-eye tracking.Medical & biological engineering & computing · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications.Brain sciences · 2024Review
- Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks.Sensors (Basel, Switzerland) · 2024Article
- Assessing cognitive workloads of assembly workers during multi-task switching.Scientific reports · 2023Article
- From brain to worksite: the role of fNIRS in cognitive studies and worker safety.Frontiers in public health · 2023Review
- EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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Abstract
backgroundMental workload is a critical consideration in complex man-machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considerable attention. However, existing EEG-fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application.
methodsThe signal acquisition configuration was optimized by analyzing the feature importance in mental workload recognition model and a more accurate and convenient EEG-fNIRS-based mental workload detection method was constructed. A classical Multi-Task Attribute Battery (MATB) task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected.
resultsA higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 76.25 ± 5.21%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O
conclusionsThe channel configuration of EEG-fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 76.25 ± 5.21% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human-computer interaction systems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.