ArticleScientific reports2018
Highly Interactive Brain-Computer Interface Based on Flicker-Free Steady-State Motion Visual Evoked Potential.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Text Sequence Stimulation for High-Speed and Comfortable SSVEP-BCI.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Beyond flickering: introducing code-modulated motion visual evoked potentials for brain-computer interfacing.Frontiers in neuroergonomics · 2026Article
- A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.Frontiers in neuroscience · 2023Article
- A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.Frontiers in neuroscience · 2023Article
- [Research advances in non-invasive brain-computer interface control strategies].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis.Sensors (Basel, Switzerland) · 2022Article
- A Zero-Padding Frequency Domain Convolutional Neural Network for SSVEP Classification.Frontiers in human neuroscience · 2022Article
- A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG.Frontiers in computational neuroscience · 2022Article
- Motion Fatigue State Detection Based on Neural Networks.Computational intelligence and neuroscience · 2022Article
- Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach.Sensors (Basel, Switzerland) · 2021Article
- Quantitative and objective diagnosis of color vision deficiencies based on steady-state visual evoked potentials.International ophthalmology · 2021Article
- Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.Frontiers in human neuroscience · 2021Review
- A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research.Computational intelligence and neuroscience · 2021Review
- Assessing the Effect of the Refresh Rate of a Device on Various Motion Stimulation Frequencies Based on Steady-State Motion Visual Evoked Potentials.Frontiers in neuroscience · 2021Article
- Decoding Covert Speech From EEG-A Comprehensive Review.Frontiers in neuroscience · 2021Review
- A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations.Computational intelligence and neuroscience · 2021Article
- Comparison of the performance of six stimulus paradigms in visual acuity assessment based on steady-state visual evoked potentials.Documenta ophthalmologica. Advances in ophthalmology · 2020Article
- Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain-Computer Interfaces.Brain sciences · 2020Article
- Assessment of Human Visual Acuity Using Visual Evoked Potential: A Review.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Visual evoked potential-based brain-computer interfaces (BCIs) have been widely investigated because of their easy system configuration and high information transfer rate (ITR). However, the uncomfortable flicker or brightness modulation of existing methods restricts the practical interactivity of BCI applications. In our study, a flicker-free steady-state motion visual evoked potential (FF-SSMVEP)-based BCI was proposed. Ring-shaped motion checkerboard patterns with oscillating expansion and contraction motions were presented by a high-refresh-rate display for visual stimuli, and the brightness of the stimuli was kept constant. Compared with SSVEPs, few harmonic responses were elicited by FF-SSMVEPs, and the frequency energy of SSMVEPs was concentrative. These FF-SSMVEPs evoked "single fundamental peak" responses after signal processing without harmonic and subharmonic peaks. More stimulation frequencies could thus be selected to elicit more responding fundamental peaks without overlap with harmonic peaks. A 40-target online SSMVEP-based BCI system was achieved that provided an ITR up to 1.52 bits per second (91.2 bits/min), and user training was not required to use this system. This study also demonstrated that the FF-SSMVEP-based BCI system has low contrast and low visual fatigue, offering a better alternative to conventional SSVEP-based BCIs.
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