Literature DB >> 17549911

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 FFT (fast Fourier transform)-based spectrum estimation method.

Mesh：

Year:  2007        PMID： 17549911     DOI： 10.1109/tbme.2006.889197

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


  55 in total

1.  High-speed spelling with a noninvasive brain-computer interface.

Authors:  Xiaogang Chen; Yijun Wang; Masaki Nakanishi; Xiaorong Gao; Tzyy-Ping Jung; Shangkai Gao
Journal:  Proc Natl Acad Sci U S A       Date:  2015-10-19       Impact factor: 11.205

2.  The performance of 9-11-year-old children using an SSVEP-based BCI for target selection.

Authors:  James J S Norton; Jessica Mullins; Birgit E Alitz; Timothy Bretl
Journal:  J Neural Eng       Date:  2018-06-28       Impact factor: 5.379

3.  Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb.

Authors:  Petar Horki; Teodoro Solis-Escalante; Christa Neuper; Gernot Müller-Putz
Journal:  Med Biol Eng Comput       Date:  2011-03-11       Impact factor: 2.602

4.  Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain-computer interfaces.

Authors:  Enzeng Dong; Changhai Li; Liting Li; Shengzhi Du; Abdelkader Nasreddine Belkacem; Chao Chen
Journal:  Med Biol Eng Comput       Date:  2017-02-25       Impact factor: 2.602

Review 5.  Guidelines for Feature Matching Assessment of Brain-Computer Interfaces for Augmentative and Alternative Communication.

Authors:  Kevin M Pitt; Jonathan S Brumberg
Journal:  Am J Speech Lang Pathol       Date:  2018-08-06       Impact factor: 2.408

6.  Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss.

Authors:  Masaki Nakanishi; Yu-Te Wang; Tzyy-Ping Jung; John K Zao; Yu-Yi Chien; Alberto Diniz-Filho; Fabio B Daga; Yuan-Pin Lin; Yijun Wang; Felipe A Medeiros
Journal:  JAMA Ophthalmol       Date:  2017-06-01       Impact factor: 7.389

7.  Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control.

Authors:  Niccolò Mora; Ilaria De Munari; Paolo Ciampolini; José Del R Millán
Journal:  Med Biol Eng Comput       Date:  2016-11-17       Impact factor: 2.602

8.  Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.

Authors:  Bradley J Edelman; Jianjun Meng; Nicholas Gulachek; Christopher C Cline; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-05       Impact factor: 3.802

9.  Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis.

Authors:  Masaki Nakanishi; Yijun Wang; Xiaogang Chen; Yu-Te Wang; Xiaorong Gao; Tzyy-Ping Jung
Journal:  IEEE Trans Biomed Eng       Date:  2017-04-19       Impact factor: 4.538

Review 10.  A survey of stimulation methods used in SSVEP-based BCIs.

Authors:  Danhua Zhu; Jordi Bieger; Gary Garcia Molina; Ronald M Aarts
Journal:  Comput Intell Neurosci       Date:  2010-03-07
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