Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Zhang, Y, Xie, SQ orcid.org/0000-0003-2641-2620, Wang, H orcid.org/0000-0002-2281-5679 et al. (2 more authors) (2021) Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review. IEEE Sensors Journal, 21 (2). pp. 1124-1138. ISSN 1530-437X

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Keywords: Brain-computer interface (BCI); steady state visual evoked potential (SSVEP); healthcare application; data analytics; canonical correlation analysis
Dates:
  • Published: 15 January 2021
  • Accepted: 9 August 2020
  • Published (online): 18 August 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/S019219/1
Depositing User: Symplectic Publications
Date Deposited: 28 Aug 2020 12:35
Last Modified: 28 Apr 2021 08:50
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Identification Number: https://doi.org/10.1109/JSEN.2020.3017491

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