ArticleFrontiers in human neuroscience2021
Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Self-Concentration Detection Based on Doubled Amplitude/Phase Processing in Node PDE Modular Models.Biomimetics (Basel, Switzerland) · 2026Article
- Neural Complexity of Implicit Attitudes Predicts Exercise Behavior in Hypertensive Patients: An EEG Entropy Study.Brain sciences · 2026Article
- MSGM: a multi-scale spatiotemporal graph Mamba for EEG emotion recognition.Frontiers in neuroscience · 2026Article
- Electroencephalogram-based multimodal attention level classification using deep learning techniques.Frontiers in human neuroscience · 2026Article
- Brain complexity represents uncertainty in sequence learning and corroborates habituation deficit in Parkinson disease patients.Scientific reports · 2025Article
- Empathic Traits Modulate Oscillatory Dynamics Revealed by Time-Frequency Analysis During Body Language Reading.Brain sciences · 2025Article
- Impact of visual distractors in virtual reality environments on sustained attention behavioral performance and EEG characteristics.Frontiers in human neuroscience · 2025Article
- Decoding influences of indoor temperature and light on neural activity: entropy analysis of electroencephalographic signals.Pflugers Archiv : European journal of physiology · 2024Article
- Closed-Loop Transcranial Electrical Neurostimulation for Sustained Attention Enhancement: A Pilot Study towards Personalized Intervention Strategies.Bioengineering (Basel, Switzerland) · 2024Article
- Radio, Podcasts, and Music Streaming-An Electroencephalography and Physiological Analysis of Listeners' Attitude, Attention, Memory, and Engagement.Brain sciences · 2024Article
- [Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Fronto-parieto-subthalamic activity decodes motor status in Parkinson's disease.CNS neuroscience & therapeutics · 2023Article
- An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features.Frontiers in neuroscience · 2023Article
- Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device.Sensors (Basel, Switzerland) · 2022Article
- Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals.Frontiers in psychology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Measuring and identifying the specific level of sustained attention during continuous tasks is essential in many applications, especially for avoiding the terrible consequences caused by reduced attention of people with special tasks. To this end, we recorded EEG signals from 42 subjects during the performance of a sustained attention task and obtained resting state and three levels of attentional states using the calibrated response time. EEG-based dynamical complexity features and Extreme Gradient Boosting (XGBoost) classifier were proposed as the classification model, Complexity-XGBoost, to distinguish multi-level attention states with improved accuracy. The maximum average accuracy of Complexity-XGBoost were 81.39 ± 1.47% for four attention levels, 80.42 ± 0.84% for three attention levels, and 95.36 ± 2.31% for two attention levels in 5-fold cross-validation. The proposed method is compared with other models of traditional EEG features and different classification algorithms, the results confirmed the effectiveness of the proposed method. We also found that the frontal EEG dynamical complexity measures were related to the changing process of response during sustained attention task. The proposed dynamical complexity approach could be helpful to recognize attention status during important tasks to improve safety and efficiency, and be useful for further brain-computer interaction research in clinical research or daily practice, such as the cognitive assessment or neural feedback treatment of individuals with attention deficit hyperactivity disorders, Alzheimer's disease, and other diseases which affect the sustained attention function.
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.