ArticleNature communications2024
Wireless ear EEG to monitor drowsiness.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Wearable Electronics for Precision Diagnosis Through Advanced Manufacturing and Integration.Nano-micro letters · 2026Review
- An intelligent electronic-textile system with adaptive sweat-regulation for continuous exercise-induced fatigue monitoring.Nature communications · 2026Article
- Personalized Classification of Scenario-Derived Operational Driver-State Classes from Non-Intrusive Wearable Signals in Real-World SAE Level 2 Automated Driving.Sensors (Basel, Switzerland) · 2026Article
- A symbiotic skin hydrogel interface enabled by flexible hydrogel network with embedded enhancement structure.Nature communications · 2026Article
- Smart Wearable EEG Devices: A Review of Lightweight, Multi-Sensor Systems for Sleep and Everyday Neurophysiology.Biosensors · 2026Review
- Convergence of Soft Electronics and Artificial Intelligence: From Materials to Intelligent Systems.Nano-micro letters · 2026Review
- [Research on auditory neurofeedback technology and its multi-disciplinary applications].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Recent Progress in In-Ear EEG Technology and Its Emerging Real-World Applications: A Review.Micromachines · 2026Review
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study.JMIR formative research · 2026Article
- Simultaneous detection of physical and mental fatigue using limited-channel EEG for practical workplace monitoring.Medical & biological engineering & computing · 2026Article
- Materials and System Design for Self-Decision Bioelectronic Systems.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Advances in (Bio)Sensors for Physiological Monitoring: A Special Issue Review.Sensors (Basel, Switzerland) · 2026Article
- Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.Nano-micro letters · 2026Review
- In-ear EEG wearables for brain activity assessment and cognitive rehabilitation: the emerging role of multimodal embedded intelligence.Frontiers in human neuroscience · 2026Review
- Unperceivable Designs of Wearable Electronics.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Seeing inside the Body Using Wearable Sensing and Imaging Technologies.Advanced healthcare materials · 2025Review
- Article
- Practical AI application in psychiatry: historical review and future directions.Molecular psychiatry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.
Indexed as
Identifiers
What OpenQuestion holds
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.