The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis
Open accessAbstract
OBJECTIVE: This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller. METHODS: Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects. RESULTS: The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task. CONCLUSION: This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI. SIGNIFICANCE: The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives
Vialatte, F.-B., Maurice, M., Dauwels, J., & Cichocki, A. (2010) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2009.11.005 · Cited by 1,066
High-speed spelling with a noninvasive brain–computer interface
Chen, X., Wang, Y., Nakanishi, M., Gao, X., Jung, T.-P., & Gao, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508080112 · Cited by 969
Self-citationVisual and Auditory Brain–Computer Interfaces
Gao, S., Wang, Y., Gao, X., & Hong, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300164 · Cited by 449
Self-citationBrain-Computer Interfaces Based on Visual Evoked Potentials
Wang, Y., Gao, X., Hong, B., Jia, C., & Gao, S. (2008) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2008.923958 · Cited by 423
Self-citationNakanishi, M., Wang, Y., Wang, Y.-T., & Jung, T.-P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140703 · Cited by 376
Self-citation
Cited by
Mahmood, M., Mzurikwao, D., Kim, Y.-S., Lee, Y., Mishra, S., Herbert, R., Duarte, A., Ang, C. S., & Yeo, W.-H. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0091-7 · Cited by 181
Comparing user-dependent and user-independent training of CNN for SSVEP BCI
Ravi, A., Beni, N. H., Manuel, J., & Jiang, N. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6a67 · Cited by 165
Guo, M., Jin, J., Jiao, Y., Wang, X., & Cichockia, A. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00024 · Cited by 18
“…(Pfurtscheller and Neuper, 2001; Wang et al., 2006; Hwang et al., 2009; Jiao et al., 2018), steady-state visual evoked potentials (SSVEP) BCI (Ortner et al., 2011; Jiao et al., 2016; Nakanishi et al., 2018; Zhang et al., 2018), hybrid BCI (Pfurtscheller et al., 2010; Li et al., 2016), and so on.”
Background
Metadata
- Authors
- Nakanishi, M., Wang, Y., Chen, X., Wang, Y.-T., Gao, X., & Jung, T.-P.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2018-01
- Volume
- 65
- Issue
- 1
- Pages
- 104-112
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2605492512
- Cited by
- 813
- References
- 5
- FWCI
- 25.62
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
