ArticleFrontiers in neuroscience2023
An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- A Wearable Multimodal Assistive Interface for Virtual Cursor Control in Stroke Survivors with Upper-Limb Impairment.Sensors (Basel, Switzerland) · 2026Article
- XGBoost Classification of Epileptic EEG Using Nonlinear Dynamical Features and SHAP.Entropy (Basel, Switzerland) · 2026Article
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- Fusion of RR Interval Dynamics and HRV Multidomain Signatures Using Multimodal Neural Models for Metabolic Syndrome Classification.Medical sciences (Basel, Switzerland) · 2026Article
- Hybrid EEG Feature Learning Method for Cross-Session Human Mental Attention State Classification.Brain sciences · 2025Article
- Brainsourcing for temporal visual attention estimation.Biomedical engineering letters · 2025Article
- From neurotechnology to the classroom: the promise of brain-computer interfaces for training systems engineers.Frontiers in human neuroscience · 2025Article
- A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System.Sensors (Basel, Switzerland) · 2024Article
- Differences in Electroencephalography Power Levels between Poor and Good Performance in Attentional Tasks.Brain sciences · 2024Article
- A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis.Frontiers in physiology · 2024Article
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
Introduction: Attention is a complex cognitive function of human brain that plays a vital role in our daily lives. Electroencephalogram (EEG) is used to measure and analyze attention due to its high temporal resolution. Although several attention recognition brain-computer interfaces (BCIs) have been proposed, there is a scarcity of studies with a sufficient number of subjects, valid paradigms, and reliable recognition analysis across subjects. Methods: In this study, we proposed a novel attention paradigm and feature fusion method to extract features, which fused time domain features, frequency domain features and nonlinear dynamics features. We then constructed an attention recognition framework for 85 subjects. Results and discussion: We achieved an intra-subject average classification accuracy of 85.05% ± 6.87% and an inter-subject average classification accuracy of 81.60% ± 9.93%, respectively. We further explored the neural patterns in attention recognition, where attention states showed less activation than non-attention states in the prefrontal and occipital areas in α, β and θ bands. The research explores, for the first time, the fusion of time domain features, frequency domain features and nonlinear dynamics features for attention recognition, providing a new understanding of attention recognition.
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.