ReviewFrontiers in neurorobotics2020
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review.
Review in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
113 citing papers in PubMed, 2 syntheses or guidelines pooled it, 469 citations in OpenAlex.
- Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.Frontiers in human neuroscience · 2024Pooled it
- Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Article
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- CNN models in time-frequency domain for identification of motor imagery tasks from EEG signals.Physical and engineering sciences in medicine · 2026Article
- Adoption of Deep Learning Methods for SSVEP Classification in XR-Based Wearable Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2026Article
- Article
- SSDLabeler: realistic semi-synthetic data generation for multi-label artifact classification in EEG.Scientific reports · 2026Article
- Conductive Hydrogel-Enabled Electrode for Scalp Electroencephalography Monitoring.Small methods · 2026Review
- Dual-Manifold Contrastive Learning for Robust and Real-Time EEG Motor Decoding.Sensors (Basel, Switzerland) · 2026Article
- An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels.Scientific data · 2026Article
- Deep Learning-Based Decoding and Feature Visualization of Motor Imagery Speeds From EEG Signals.IEEE open journal of engineering in medicine and biology · 2026Article
- A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces.Scientific data · 2025Article
- Skin-Conformal Hydrogel-Based Electroencephalography Electrodes with Surfactant-Reorganized PEDOT:PSS.Materials (Basel, Switzerland) · 2025Article
- Error-related potentials in EEG signals: feature-based detection for human-robot interaction.Scientific reports · 2025Article
- Motor Imagery Acquisition Paradigms: In the Search to Improve Classification Accuracy.Sensors (Basel, Switzerland) · 2025Article
- Smart closed-loop drug delivery systems.Nature reviews bioengineering · 2025Article
- [Motor imagery classification based on dynamic multi-scale convolution and multi-head temporal attention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- A review of hybrid EEG-based multimodal human-computer interfaces using deep learning: applications, advances, and challenges.Biomedical engineering letters · 2025Review
- Machine learning classification of active viewing of pain and non-pain images using EEG does not exceed chance in external validation samples.Cognitive, affective & behavioral neuroscience · 2025Article
53 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
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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.