An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method

By Guangyu Bin, Xiaorong Gao, Zheng Yan, Bo Hong, Shangkai Gao. Journal of Neural Engineering, 2009

792 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2f0b470c9d57e4038133c21a5bd5e8954b7b1987

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface (2015, methodology): “Each visual target flickers at a unique frequency, allowing a CCA-based classifier to determine the user’s desired character.”
A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials (2015, methodology): “Among these methods, CCA is one of the most widely used methods in SSVEP-based BCIs due to its high efficiency, robustness, and simple implementation [10, 11, 15, 21 – 23].”
P300 brain computer interface: current challenges and emerging trends (2012, positive): “Just as future SSVEP BCIs might more frequently employ newer stimulation approaches (Bin et al., 2009, 2011), P300 BCIs might also start using stimuli that move or otherwise change in ways that elicit more distinct ERPs.”
A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces (2017, methodology): “The canonical correlation analysis (CCA) method has been widely used in frequency detection of SSVEPs [15].”
A high-speed BCI based on code modulation VEP (2011, methodology): “This involves finding linear transformations Wx and Ws which maximize the correlation between projected X and S [6]:”
A High-Speed Brain Speller using steady-State Visual evoked potentials (2014, methodology): “16 The highest ITR reported in SSVEP-based BCIs, which employed frequency coding in system design, was 105 bits/min.”
Brain-Computer Interfaces: A Gentle Introduction (2009, background): “There are a few papers that report BCIs with a high ITR, ranging from 30 bits/min [26, 27] to slightly above 60 bits/min [28] and, most recently, over 90 bits per minute [29].”
A Survey of Stimulation Methods Used in SSVEP-Based BCIs (2010, neutral): “5 bits/min with 8 choices [57], and a system using rectangle stimuli obtained a bit rate of 58 bits/min with 6 choices [45].”
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, background): “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].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Users modulate these responses to produce a BMI output (10, 42, 70, 74, 101, 117, 521).”
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, methodology): “Recently, the CCA-based frequency detection method has been widely used due to its high ef ﬁ ciency, robustness, and simple implementation (Bin et al 2009).”