ArticleFrontiers in human neuroscience2022
Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06485115 (The Effectiveness of Combining a Home-based Digital Motor Telerehabilitation Program With Conventional Therapy in Progressive Multiple Sclerosis), which is not on this map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Effectiveness of Combining a Home-based Digital Motor Telerehabilitation Program With Conventional Therapy in Progressive Multiple Sclerosis: a Multicentre, Randomized Controlled Trial
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review.Frontiers in human neuroscience · 2022Pooled it
- Around the Clock: Wearing a Flex-Printed trEEGrid Electrode Patch and Miniaturized Amplifier for Nearly 24 Hours Across Daytime and Overnight EEG Sessions.Sensors (Basel, Switzerland) · 2026Article
- Mapping the Human Performance Envelope Through Multivariate Information Transfer.Brain sciences · 2026Article
- Explainable artificial intelligence in air traffic control: effects of expertise on workload, acceptance, and usage intentions.Brain informatics · 2026Article
- A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.PloS one · 2026Article
- Multi-method characterization of neurophysiological and biological stress responses in surgical teams during real surgical procedures.Frontiers in neuroergonomics · 2026Article
- Beyond the lab: real-world benchmarking of wearable EEGs for passive brain-computer interfaces.Brain informatics · 2025Article
- Analysis of Neurophysiological Correlates of Mental Fatigue in Both Monotonous and Demanding Driving Conditions.Brain sciences · 2025Article
- Brain Cortical Area Characterization and Machine Learning-Based Measure of Rasmussen's S-R-K Model.Brain sciences · 2025Article
- A Multimodal Neurophysiological Approach to Evaluate Educational Contents in Terms of Cognitive Processes and Engagement.Bioengineering (Basel, Switzerland) · 2025Article
- Amputees but not healthy subjects optimally integrate non-spatially matched visuo-tactile stimuli.iScience · 2025Article
- Mind the road: attention related neuromarkers during automated and manual simulated driving captured with a new mobile EEG sensor system.Frontiers in neuroergonomics · 2025Article
- Mutual information-based teamwork evaluation in real-world environments: an exploratory investigation with professional surgeons.Frontiers in network physiology · 2025Article
- Beyond the time-on-task: an EEG-driven approach for effective physiological assessment of mental fatigue in simulated and real driving.Frontiers in bioengineering and biotechnology · 2025Article
- Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task.Behavioral sciences (Basel, Switzerland) · 2024Article
- Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.Sensors (Basel, Switzerland) · 2024Article
- A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions.Brain sciences · 2024Article
- Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study.JMIR research protocols · 2024Article
- Reproducible machine learning research in mental workload classification using EEG.Frontiers in neuroergonomics · 2024Review
- Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment.Frontiers in neuroinformatics · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.
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Registered trials
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.