ReviewSensors (Basel, Switzerland)2025
A Review of Developments in Carbon-Based Nanocomposite Electrodes for Noninvasive Electroencephalography.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- A systematic evaluation of EEG electrode geometry for enhanced signals: an experimental approach.Scientific reports · 2026Article
- Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges.Journal of chemical information and modeling · 2026Review
- Advances in (Bio)Sensors for Physiological Monitoring: A Special Issue Review.Sensors (Basel, Switzerland) · 2026Article
- Universal Interatomic Potentials with DFT for Understanding Orbital Localization in Polydimethylsiloxane-Amorphous Silica Nanocomposites.ACS omega · 2025Article
- Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable biosensors have been of interest for their wide range of uses, varying from recording biological signals to measuring strain of bending joints. Carbon nanoparticles have been utilized in biocompatible polymers to create nanocomposites with highly tunable mechanical and electrical properties. These nanocomposites have been demonstrated to be highly effective as wearable sensors for recording physiological signals such as electroencephalography (EEG), offering advantages in mechanical and electrical properties and signal quality over commercially available sensors while maintaining feasibility and scalability in manufacturing. This review aims to provide a critical summary of the recent literature on the properties, design, fabrication, and performance of carbon-based nanocomposites for EEG electrodes. The goal of this review is to highlight the various design configurations and properties thereof, manufacturing methods, performance measurements, and related challenges associated with these promising noninvasive dry soft electrodes. While this technology offers many advantages over either other noninvasive or their invasive counterparts, there are still various challenges and opportunities for improvements and innovation. For example, the investigation of gradient composite structures, hybrid nanocomposite/composite materials, hierarchical contact surfaces, and the influence of loading and alignment of the dispersal phase in the performance of these electrodes could lead to novel and better designs. Finally, current practices for evaluating the performance of novel EEG electrodes are discussed and challenged, emphasizing the critical need for the development of standardized assessment protocols, which could provide reliability in the field, enable benchmarking, and hence promote innovation.
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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.