ArticleFrontiers in human neuroscience2023
A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles.
Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Commercial Metallized Textiles for Dry EEG Electrodes: Structure, Textile-Level Durability, and Controlled Frontal Validation Against Ag/AgCl.Sensors (Basel, Switzerland) · 2026Article
- Smart Wearable EEG Devices: A Review of Lightweight, Multi-Sensor Systems for Sleep and Everyday Neurophysiology.Biosensors · 2026Review
- Wrinkle-Adaptive Kirigami Wearables With Anisotropic Deformability for Sleep EEG Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Digital phenotyping for assessment and prediction of interoception, chronic stress, and self-regulation in adults: a scoping review.Frontiers in digital health · 2026Review
- Design and validation of a modular smart headband with embroidered electrodes for comfortable EEG monitoring.PloS one · 2026Article
- Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.Ageing research reviews · 2025Review
- Passive Brain-Computer Interface Using Textile-Based Electroencephalography.Sensors (Basel, Switzerland) · 2025Article
- Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Review
- Measuring Blink-Related Brainwaves Using Low-Density Electroencephalography with Textile Electrodes for Real-World Applications.Sensors (Basel, Switzerland) · 2025Article
- Smart Textile Technology for the Monitoring of Mental Health.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.
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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.