Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs

By Zhonglin Lin, Changshui Zhang, Wei Wu, Xiaorong Gao. IEEE Transactions on Biomedical Engineering, 2006

1,126 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/66f70f08115cbeb3675d7127cbcfc2577b601167

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Citations in the corpus

Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis (2013, methodology): “In this study, the proposed MsetCCA method is compared with the CCA method 21 , the MwayCCA method 26 and the PCCA method 28 , to validate its eﬀectiveness for SSVEP frequency recognition.”
How many people are able to control a P300-based brain-computer interface (BCI)? (2009, neutral): “, robot control or mobile phone control [8].”
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, background): “For example, spatial ﬁ ltering approaches such as the canonical correlation analysis (CCA) (Lin et al 2007) and minimum energy combination (MEC) (Friman et al 2007) methods have been developed to enhance the SNR of SSVEPs.”
A High-Speed Brain Speller using steady-State Visual evoked potentials (2014, methodology): “25 The CCA-based method has been further improved in several recent studies.”
An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method (2009, methodology): “Lin et al [11] ﬁrst proposed the use of the CCA method for multi-channel SSVEP detection.”
Brain-Computer Interfaces Based on Visual Evoked Potentials (2008, methodology): “Under this circumstance, it is acceptable to use more electrodes to acquire more sufficient data to fulfill the detection of SSVEP signals with multichannel data analysis approaches, e.g., spatial filtering techniques described in [28] and the canonical correlation analysis method presented in [ 29 ”
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, methodology): “Recent studies have suggested that spatial filtering techniques are effective to remove background EEG signals, leading to enhanced SNR and discriminability of SSVEPs [13], [14].”
A Survey of Stimulation Methods Used in SSVEP-Based BCIs (2010, neutral): “2007 [53] CRT Squares 27, 29, .”
A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials (2015, methodology): “More details for each method can be found in the corresponding references [5, 15, 29 – 33].”