ReviewCognitive neurodynamics2021
Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.
Review in Cognitive neurodynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 papers.
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
30 citing papers in PubMed.
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Evaluation of video background and stimulus transparency in a visual ERP-based BCI under RSVP.Medical & biological engineering & computing · 2026Article
- Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.Journal of neuroengineering and rehabilitation · 2026Article
- An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.Scientific data · 2025Article
- Advancements in the application of brain-computer interfaces based on different paradigms in amyotrophic lateral sclerosis.Frontiers in neuroscience · 2025Review
- A hierarchical Bayesian inference model for volatile multivariate exponentially distributed signals.Frontiers in computational neuroscience · 2025Article
- Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.PeerJ. Computer science · 2025Article
- Assessing the influence of latency variability on EEG classifiers - a case study of face repetition priming.Cognitive neurodynamics · 2024Article
- MSHANet: a multi-scale residual network with hybrid attention for motor imagery EEG decoding.Cognitive neurodynamics · 2024Article
- A delayed matching task-based study on action sequence of motor imagery.Cognitive neurodynamics · 2024Article
- Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.Journal of neuroengineering and rehabilitation · 2024Article
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.Frontiers in neuroscience · 2024Article
- Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.Frontiers in neuroscience · 2024Article
- Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system.Medical & biological engineering & computing · 2023Article
- A novel noninvasive brain-computer interface by imagining isometric force levels.Cognitive neurodynamics · 2023Article
- Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Evaluation of Single-Trial Classification to Control a Visual ERP-BCI under a Situation Awareness Scenario.Brain sciences · 2023Article
- Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm.Cognitive neurodynamics · 2023Article
- An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.Brain sciences · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.
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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.