ArticleCognitive neurodynamics2020
Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification.
Article in Cognitive neurodynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.Medical & biological engineering & computing · 2025Article
- A novel hybrid method based on task-related component and canonical correlation analyses (H-TRCCA) for enhancing SSVEP recognition.Frontiers in neuroscience · 2025Article
- Brain-computer Interaction in the Smart Era.Current medical science · 2024Review
- Time-frequency feature extraction based on multivariable synchronization index for training-free SSVEP-based BCI.Cognitive neurodynamics · 2024Article
- Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.BMC bioinformatics · 2024Article
- Multi-information improves the performance of CCA-based SSVEP classification.Cognitive neurodynamics · 2024Article
- [Progresses and prospects on frequency recognition methods for steady-state visual evoked potential].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Review
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2 authors.
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Abstract
Canonical correlation analysis (CCA) method and its extended methods have been widely and successfully applied to the frequency recognition in SSVEP-based BCI systems. As a state-of-the-art extended method, filter bank canonical correlation analysis has higher accuracy and information transmission rate (ITR) than CCA. However, in the CCA method, the temporally local structure of samples has not been well considered. In this correspondence, we proposed termed temporally local canonical correlation analysis (TCCA). In this new method, the original covariance matrix was replaced by the temporally local covariance matrix. Furthermore, we proposed an improved frequency identification method of filter bank based on TCCA, named filter bank temporally local canonical correlation analysis (FBTCCA). In the offline environment, we used a leave-one-subject-out validation strategy on datasets of ten testees to optimize the parameters of TCCA and FBTCCA and evaluate the two algorithms. The experimental results affirm that TCCA markedly outperformed CCA, and FBTCCA obtained the highest accuracy among the four methods. This study corroborates that TCCA-based approaches have great potential for implementing short time window SSVEP-based BCI systems.
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