ArticleSensors (Basel, Switzerland)2020
Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 28 citations in OpenAlex.
- Recent applications of EEG-based brain-computer-interface in the medical field.Military Medical Research · 2025Review
- The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.Cognitive neurodynamics · 2024Article
- Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.BMC bioinformatics · 2024Article
- Review
- Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.Sensors (Basel, Switzerland) · 2022Article
- Instant classification for the spatially-coded BCI.PloS one · 2022Article
- Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.Brain sciences · 2021Review
- Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.Cognitive neurodynamics · 2021Review
- Evaluating the Effect of Stimuli Color and Frequency on SSVEP.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The brain-computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.
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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.