Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis-Reference-Cited by-同舟云学术

Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis

Published:2023 Issue: Volume:31 Page:1343-1352
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Wong Chi Man¹^ORCID,Wang Ze¹^ORCID,Wang Boyu²^ORCID,Rosa Agostinho³^ORCID,Jung Tzyy-Ping⁴^ORCID,Wan Feng¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Centre for Cognitive and Brain Sciences, the Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, Faculty of Science and Technology, University of Macau, Taipa, Macau

2. Department of Computer Science and the Brain Mind Institute, University of Western Ontario, London, ON, Canada

3. ISR and DBE-IST, Universidade de Lisboa, Lisbon, Portugal

4. Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA

Funder

The Science and Technology Development Fund, Macau SAR

The University of Macau Research Committee

The Natural Science Foundation of Guangdong Province, China

Laboratory for Robotics and Engineering Systems-Fundação para a Ciência e a Tecnologia (LARSyS-FCT) Plurianual

Natural Sciences and Engineering Research Council of Canada

high performance computing cluster

information and communication technology office (ICTO) of the University of Macau

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/10031624/10040695.pdf?arnumber=10040695

Reference47 articles.

1. Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI

2. Optimizing a dual-frequency and phase modulation method for SSVEP-based BCIs

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

4. Online Adaptation Boosts SSVEP-Based BCI Performance

5. SSVEP-Based Brain–Computer Interfaces Using FSK-Modulated Visual Stimuli

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3. Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm;Sensors;2023-07-11

4. A SSVEP-Based Brain–Computer Interface With Low-Pixel Density of Stimuli;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023