Literature DB >> 17152442

Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.

Zhonglin Lin1, Changshui Zhang, Wei Wu, Xiaorong Gao.   

Abstract

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.

Mesh：

Year:  2006        PMID： 17152442     DOI： 10.1109/tbme.2006.886577

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN： 0018-9294            Impact factor:   4.538


  44 in total

1.  A multi-day and multi-band dataset for a steady-state visual-evoked potential-based brain-computer interface.

Authors:  Ga-Young Choi; Chang-Hee Han; Young-Jin Jung; Han-Jeong Hwang
Journal:  Gigascience       Date:  2019-11-01       Impact factor: 6.524

2.  SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI.

Authors:  Keith W Jamison; Abhrajeet V Roy; Sheng He; Stephen A Engel; Bin He
Journal:  J Neurosci Methods       Date:  2015-01-30       Impact factor: 2.390

3.  EEG-based hybrid QWERTY mental speller with high information transfer rate.

Authors:  Er Akshay Katyal; Rajesh Singla
Journal:  Med Biol Eng Comput       Date:  2021-02-16       Impact factor: 2.602

4.  Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index.

Authors:  Yangsong Zhang; Daqing Guo; Peng Xu; Yu Zhang; Dezhong Yao
Journal:  Cogn Neurodyn       Date:  2016-07-19       Impact factor: 5.082

5.  Recursive Bayesian Coding for BCIs.

Authors:  Matt Higger; Fernando Quivira; Murat Akcakaya; Mohammad Moghadamfalahi; Hooman Nezamfar; Mujdat Cetin; Deniz Erdogmus
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2016-07-13       Impact factor: 3.802

Review 6.  Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI.

Authors:  S Chevallier; E K Kalunga; Q Barthélemy; E Monacelli
Journal:  Neuroinformatics       Date:  2021-01

7.  A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG.

Authors:  Wei Wu; Zhe Chen; Shangkai Gao; Emery N Brown
Journal:  Neuroimage       Date:  2011-03-21       Impact factor: 6.556

8.  Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling.

Authors:  Rogers F Silva; Sergey M Plis; Jing Sui; Marios S Pattichis; Tülay Adalı; Vince D Calhoun
Journal:  IEEE J Sel Top Signal Process       Date:  2016-07-27       Impact factor: 6.856

Review 9.  A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces.

Authors:  Wonjun Ko; Eunjin Jeon; Seungwoo Jeong; Jaeun Phyo; Heung-Il Suk
Journal:  Front Hum Neurosci       Date:  2021-05-28       Impact factor: 3.169

10.  Sample-poor estimation of order and common signal subspace with application to fusion of medical imaging data.

Authors:  Yuri Levin-Schwartz; Yang Song; Peter J Schreier; Vince D Calhoun; Tülay Adalı
Journal:  Neuroimage       Date:  2016-03-31       Impact factor: 6.556
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