ArticleScientific reports2019
Asynchronous non-invasive high-speed BCI speller with robust non-control state detection.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Review
- Neural interfaces: Bridging the brain to the world beyond healthcare.Exploration (Beijing, China) · 2024Article
- [The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).Brain sciences · 2024Article
- A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.iScience · 2024Article
- A Bayesian dynamic stopping method for evoked response brain-computer interfacing.Frontiers in human neuroscience · 2024Article
- Decoding of the neural representation of the visual RGB color model.PeerJ. Computer science · 2023Article
- Online adaptive group-wise sparse Penalized Recursive Exponentially Weighted N-way Partial Least Square for epidural intracranial BCI.Frontiers in human neuroscience · 2023Article
- A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.Frontiers in neuroscience · 2023Review
- Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience.Frontiers in human neuroscience · 2023Article
- Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces.APL bioengineering · 2021Review
- Asynchronous c-VEP communication tools-efficiency comparison of low-target, multi-target and dictionary-assisted BCI spellers.Scientific reports · 2020Article
- A Robust Screen-Free Brain-Computer Interface for Robotic Object Selection.Frontiers in robotics and AI · 2020Article
- Five Shades of Grey: Exploring QuintaryComputational intelligence and neuroscience · 2020Article
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2 authors.
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Abstract
Brain-Computer Interfaces (BCIs) enable users to control a computer by using pure brain activity. Recent BCIs based on visual evoked potentials (VEPs) have shown to be suitable for high-speed communication. However, all recent high-speed BCIs are synchronous, which means that the system works with fixed time slots so that the user is not able to select a command at his own convenience, which poses a problem in real-world applications. In this paper, we present the first asynchronous high-speed BCI with robust distinction between intentional control (IC) and non-control (NC), with a nearly perfect NC state detection of only 0.075 erroneous classifications per minute. The resulting asynchronous speller achieved an average information transfer rate (ITR) of 122.7 bit/min using a 32 target matrix-keyboard. Since the method is based on random stimulation patterns it allows to use an arbitrary number of targets for any application purpose, which was shown by using an 55 target German QWERTZ-keyboard layout which allowed the participants to write an average of 16.1 (up to 30.7) correct case-sensitive letters per minute. As the presented system is the first asynchronous high-speed BCI speller with a robust non-control state detection, it is an important step for moving BCI applications out of the lab and into real-life.
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