ArticleBrain sciences2020
In-Ear EEG Based Attention State Classification Using Echo State Network.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 45 citations in OpenAlex.
- Wearable Morphology-Adaptive in-Ear Device With Active Cleansing Capability for Multimodal Health Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Recent Progress in In-Ear EEG Technology and Its Emerging Real-World Applications: A Review.Micromachines · 2026Review
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Electroencephalogram-based multimodal attention level classification using deep learning techniques.Frontiers in human neuroscience · 2026Article
- In-ear EEG wearables for brain activity assessment and cognitive rehabilitation: the emerging role of multimodal embedded intelligence.Frontiers in human neuroscience · 2026Review
- Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review.Fundamental research · 2025Review
- Hearables: Bioelectronics technological challenges and opportunities.Wearable technologies · 2025Review
- Neuroergonomic Attention Assessment in Safety-Critical Tasks: EEG Indices and Subjective Metrics Validation in a Novel Task-Embedded Reaction Time Paradigm.Brain sciences · 2024Article
- A Versatile In-Ear Biosensing System and Body-Area Network for Unobtrusive Continuous Health Monitoring.IEEE transactions on biomedical circuits and systems · 2023Article
- Review
- A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal.International journal of environmental research and public health · 2022Article
- A simplified design of a cEEGrid ear-electrode adapter for the OpenBCI biosensing platform.HardwareX · 2022Article
- A Comparative Study on the Influence of Undersampling and Oversampling Techniques for the Classification of Physical Activities Using an Imbalanced Accelerometer Dataset.Healthcare (Basel, Switzerland) · 2022Article
- Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting.Frontiers in human neuroscience · 2021Article
- Transcranial Auricular Vagus Nerve Stimulation (taVNS) and Ear-EEG: Potential for Closed-Loop Portable Non-invasive Brain Stimulation.Frontiers in human neuroscience · 2021Article
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 at 1 institution in 1 country.
Funding
Abstract
It is important to maintain attention when carrying out significant daily-life tasks that require high levels of safety and efficiency. Since degradation of attention can sometimes have dire consequences, various brain activity measurement devices such as electroencephalography (EEG) systems have been used to monitor attention states in individuals. However, conventional EEG instruments have limited utility in daily life because they are uncomfortable to wear. Thus, this study was designed to investigate the possibility of discriminating between the attentive and resting states using in-ear EEG signals for potential application via portable, convenient earphone-shaped EEG instruments. We recorded both on-scalp and in-ear EEG signals from 6 subjects in a state of attentiveness during the performance of a visual vigilance task. We have designed and developed in-ear EEG electrodes customized by modelling both the left and right ear canals of the subjects. We use an echo state network (ESN), a powerful type of machine learning algorithm, to discriminate attention states on the basis of in-ear EEGs. We have found that the maximum average accuracy of the ESN method in discriminating between attentive and resting states is approximately 81.16% with optimal network parameters. This study suggests that portable in-ear EEG devices and an ESN can be used to monitor attention states during significant tasks to enhance safety and efficiency.
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.