CCA-Based Compressive Sensing for SSVEP-Based Brain-Computer Interfaces to Command a Robotic Wheelchair-Reference-Cited by-同舟云学术

CCA-Based Compressive Sensing for SSVEP-Based Brain-Computer Interfaces to Command a Robotic Wheelchair

Published:2022 Issue: Volume:71 Page:1-10
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Rivera-Flor Hamilton¹^ORCID,Gurve Dharmendra²^ORCID,Floriano Alan³^ORCID,Delisle-Rodriguez Denis⁴^ORCID,Mello Ricardo¹^ORCID,Bastos-Filho Teodiano¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Federal University of Espírito Santo, Vitória, Brazil

2. Department of Electrical Engineering, Ryerson University, Toronto, ON, Canada

3. Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil

4. Edmond and Lily Safra International Institute of Neurosciences, Macaíba, Brazil

Funder

Federal University of Espírito Santo

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)/Brazil

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09933046.pdf?arnumber=9933046

Reference43 articles.

1. A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces

2. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface

3. Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis

4. Inter- and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-Based BCIs

5. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs

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