ReviewMedical & biological engineering & computing2024
The role of eye movement signals in non-invasive brain-computer interface typing system.
Review in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis 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.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.Frontiers in human neuroscience · 2024Pooled it
- ICTD: Combination of Improved CNN-Transformer and Enhanced Deep Canonical Correlation Analysis for Eye-Movement Emotion Classification.Brain sciences · 2026Article
- Intersession Robust Hybrid Brain-Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism.Micromachines · 2025Article
- Supporting effective alternative access for individuals with physical disabilities: state of the science, emerging technologies, and future research directions.Augmentative and alternative communication (Baltimore, Md. : 1985) · 2025Review
- EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces.Sensors (Basel, Switzerland) · 2025Article
- A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.Micromachines · 2025Article
- Artifact filtering application to increase online parity in a communication BCI: progress toward use in daily-life.Frontiers in human neuroscience · 2025Article
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
Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.
Indexed as
Identifiers
38509350What 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.