The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces
Abstract
In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed.
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Citations
Cites
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Cited by
The steady-state visual evoked potential in vision research: A review
Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015) · Journal of Vision · doi:10.1167/15.6.4 · Cited by 1,234
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
Bin, G., Gao, X., Yan, Z., Hong, B., & Gao, S. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/4/046002 · Cited by 773
How many people are able to control a P300-based brain-computer interface (BCI)?
Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., & Edlinger, G. (2009) · Neuroscience Letters · doi:10.1016/j.neulet.2009.06.045 · Cited by 652
A Survey of Stimulation Methods Used in SSVEP-Based BCIs
Zhu, D., Bieger, J., Garcia Molina, G., & Aarts, R. M. (2010) · Computational Intelligence and Neuroscience · doi:10.1155/2010/702357 · Cited by 654
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016
Wu, D., Xu, Y., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3007453 · Cited by 434
Brain-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
A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control
Li, Y., Pan, J., Wang, F., & Yu, Z. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2270283 · Cited by 421
Nakanishi, M., Wang, Y., Wang, Y.-T., & Jung, T.-P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140703 · Cited by 376
FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS
ZHANG, Y., ZHOU, G., JIN, J., WANG, X., & CICHOCKI, A. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065714500130 · Cited by 357
BCI demographics: how many (and what kinds of) people can use an SSVEP BCI?
Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2039495 · Cited by 344
Self-citationHow Many People Could Use an SSVEP BCI?
Guger, C., Allison, B. Z., Großwindhager, B., Prückl, R., Hintermüller, C., Kapeller, C., Bruckner, M., Krausz, G., & Edlinger, G. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00169 · Cited by 176
Wei, W., Qiu, S., Zhang, Y., Mao, J., & He, H. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac5eb7 · Cited by 24
Brain-computer interface: a possible help for people with locked-in syndrome,
Nolte, A. (2021) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-72254-8_24 · Cited by 4
Metadata
- Authors
- Friman, O., Volosyak, I., & Graser, A.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2007-04
- Volume
- 54
- Issue
- 4
- Pages
- 742-750
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2122515425
- Cited by
- 541
- References
- 1
- FWCI
- 10.36
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
