ReviewBrain sciences2021
Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance.
Review in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis.Frontiers in human neuroscience · 2023Pooled it
- Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain-computer interface solutions.Scientific reports · 2026Article
- [A scientific definition of brain-computer interfaces (BCIs): Essential components, fundamental characteristics, capability boundaries, and scope delimitation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- The ASME-speller: 30-class auditory brain-computer interface speller using stream segregation and the QWERTY layout.Frontiers in human neuroscience · 2026Article
- Brain-computer interface: an update for the clinicians.Frontiers in human neuroscience · 2026Review
- Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset.Scientific data · 2025Article
- High-Resolution EEG Source Reconstruction from PCA-Corrected BEM-FMM Reciprocal Basis Funcions: A Study with Visual Evoked Potentials from Intermittent Photic Stimulation.bioRxiv : the preprint server for biology · 2025Article
- Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.Brain sciences · 2025Article
- A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.Micromachines · 2025Article
- Toward brain-computer interface speller with movement-related cortical potentials as control signals.Frontiers in human neuroscience · 2025Article
- Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.Sensors (Basel, Switzerland) · 2024Article
- Article
- Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).Brain sciences · 2024Article
- Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.Sensors (Basel, Switzerland) · 2024Article
- Article
- Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces.Frontiers in human neuroscience · 2024Review
- EEG-based BCI Dataset of Semantic Concepts for Imagination and Perception Tasks.Scientific data · 2023Article
- Identifying Thematics in a Brain-Computer Interface Research.Computational intelligence and neuroscience · 2023Review
- A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode.Frontiers in computational neuroscience · 2023Article
- An online hybrid BCI combining SSVEP and EOG-based eye movements.Frontiers in human neuroscience · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain-computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.
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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.