The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS
Open accessAbstract
Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.
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Cites
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RELATIONS BETWEEN TWO SETS OF VARIATES
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Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
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A P300-based brain–computer interface: Initial tests by ALS patients
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Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces
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Cited by
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
Self-citationHigh-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
Nakanishi, M., Wang, Y., Wang, Y.-T., & Jung, T.-P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140703 · Cited by 376
Hong, K.-S., & Khan, M. J. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00035 · Cited by 267
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
Metadata
- Authors
- ZHANG, Y., ZHOU, G., JIN, J., WANG, X., & CICHOCKI, A.
- Venue
- International Journal of Neural Systems
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 2014-04-03
- Volume
- 24
- Issue
- 04
- Pages
- 1450013
- ISSN
- 0129-0657 (ISSN-L)
- 1793-6462
- OpenAlex
- W2151297119
- Cited by
- 357
- References
- 49
- FWCI
- 12.12
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS. https://app.cochat.ai/papers/10.1142/s0129065714500130
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- ZHANG, Y., ZHOU, G., JIN, J., WANG, X., & CICHOCKI, A. (2014). FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS. International Journal of Neural Systems. https://doi.org/10.1142/s0129065714500130
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