ArticleThe clinical respiratory journal2026
Rapid Nasal Breathing as a Biometric Trigger: High-Accuracy Electroencephalogram-Based Authentication for Clinical Applications.
Article in The clinical respiratory journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- EEG Subject Identification and Open-Set Rejection Across Paradigms.Sensors (Basel, Switzerland) · 2026Article
- Interpretable Spectral Features for Cross-Session EEG Biometric Identification and Verification.Sensors (Basel, Switzerland) · 2026Article
- Rapid Nasal Breathing as a Biometric Trigger: High-Accuracy Electroencephalogram-Based Authentication for Clinical Applications.The clinical respiratory journal · 2026Article
- Design and validation of a modular smart headband with embroidered electrodes for comfortable EEG monitoring.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundTraditional biometric systems are vulnerable to forgery, highlighting the need for secure alternatives. Electroencephalography (EEG) offers inherent advantages in liveness detection and antispoofing but typically requires external stimuli. We propose a novel paradigm leveraging intrinsic respiratory-evoked EEG signals for identity authentication, with potential applications in clinical settings where unobtrusive monitoring is critical.
methodsWe developed a 64-channel EEG acquisition system with synchronized respiratory event monitoring. Thirteen healthy volunteers performed four breathing patterns: oral, nasal, slow nasal, and rapid nasal breathing. A hybrid deep learning model was designed to optimize spatial-temporal feature extraction from EEG signals.
resultsThe model achieved 98.3% accuracy in identity recognition using rapid nasal breathing-evoked EEG, outperforming traditional biometric methods. Nasal breathing patterns consistently yielded higher accuracy than oral breathing, with rapid nasal breathing showing the strongest discriminative power.
conclusionsRespiratory-evoked EEG signals provide a viable, noninvasive biometric identifier. The high accuracy of rapid nasal breathing opens avenues for clinical integration, such as continuous patient authentication in respiratory monitoring devices or secure access to electronic health records.
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.