ArticleFrontiers in human neuroscience2016
Exploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data.
Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed.
- Identifying and Engaging Neuronal Oscillations by Transcranial Alternating Current Stimulation in Patients With Chronic Low Back Pain: A Randomized, Crossover, Double-Blind, Sham-Controlled Pilot Study.The journal of pain · 2019Trial
- Targeting reduced neural oscillations in patients with schizophrenia by transcranial alternating current stimulation.NeuroImage · 2019Trial
- Dual-Stream Fusion of Eye-Tracking and ECG Signals for Fatigue Detection in Remote Tower Air Traffic Controllers.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative Prefrontal Multimodal Physiological Signatures Under Active- and Passive-Fatigue-Inducing Simulated Driving Paradigms.Brain sciences · 2026Article
- Remote Tower Air Traffic Controller Multimodal Fatigue Detection.Sensors (Basel, Switzerland) · 2026Article
- Cognitive state monitoring for neuroadaptive information visualization.Frontiers in human neuroscience · 2026Review
- Toward practical BCIs: a BMNABC-based feature selection and sensor optimization framework for implicit learning detection from multimodal EEG-fNIRS data.Frontiers in human neuroscience · 2026Article
- Identifying neural correlates of cognitive workload in high-performance motorsport simulation: an integrated EEG and telemetry analysis of driver performance.Frontiers in neuroergonomics · 2026Article
- Article
- Closed-loop EEG-based neurofeedback and brain-computer interface interventions for mental health: a review.Frontiers in neuroscience · 2026Review
- Current Practices for Mental Fatigue Quantification and Induction in Movement Science: Introducing the SPeCIFY Guidelines.Sports medicine (Auckland, N.Z.) · 2025Review
- Simultaneous Dataset of Brain, Eye and Hand during Visuomotor Tasks.Scientific data · 2025Article
- The state of the art in assessing mental fatigue in the cockpit using head-worn sensing technology.Frontiers in neuroergonomics · 2025Review
- Development of a neural efficiency metric to assess human-exoskeleton adaptations.Frontiers in robotics and AI · 2025Article
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Cognitive workload estimation using physiological measures: a review.Cognitive neurodynamics · 2024Article
- Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments.Sensors (Basel, Switzerland) · 2024Article
- Human Attention Restoration, Flow, and Creativity: A Conceptual Integration.Journal of imaging · 2024Review
- Surgical stress: the muscle and cognitive demands of robotic and laparoscopic surgery.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2023Article
- Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation.Scientific reports · 2023Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
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Abstract
Investigations of the neuro-physiological correlates of mental loads, or states, have attracted significant attention recently, as it is particularly important to evaluate mental fatigue in drivers operating a motor vehicle. In this research, we collected multimodal EEG/ECG/EOG and fNIRS data simultaneously to develop algorithms to explore neuro-physiological correlates of drivers' mental states. Each subject performed simulated driving under two different conditions (well-rested and sleep-deprived) on different days. During the experiment, we used 68 electrodes for EEG/ECG/EOG and 8 channels for fNIRS recordings. We extracted the prominent features of each modality to distinguish between the well-rested and sleep-deprived conditions, and all multimodal features, except EOG, were combined to quantify mental fatigue during driving. Finally, a novel driving condition level (DCL) was proposed that distinguished clearly between the features of well-rested and sleep-deprived conditions. This proposed DCL measure may be applicable to real-time monitoring of the mental states of vehicle drivers. Further, the combination of methods based on each classifier yielded substantial improvements in the classification accuracy between these two conditions.
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