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[Progresses and prospects on frequency recognition methods for steady-state visual evoked potential].

Yangsong Zhang1,2, Min Xia1, Ke Chen2

  • 1School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan 621010, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|March 1, 2022
PubMed
Summary
This summary is machine-generated.

This review summarizes recent advances in frequency recognition algorithms for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs). It covers unsupervised, supervised, and deep learning methods, exploring future research directions in BCI technology.

Keywords:
Deep learningElectroencephalogramFrequency recognitionMachine learningSteady-state visual evoked potential

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Steady-state visual evoked potential (SSVEP) is a key control signal in brain-computer interface (BCI) systems.
  • SSVEP-based BCIs offer high data transfer rates and minimal user training, making them a significant research area.
  • Advancements in algorithms are crucial for improving the performance and usability of SSVEP BCIs.

Purpose of the Study:

  • To provide a comprehensive overview of recent frequency recognition algorithms for SSVEP.
  • To categorize and analyze algorithms into unsupervised, supervised, and deep learning approaches.
  • To identify emerging trends and future research avenues in SSVEP-based BCI.

Main Methods:

  • Systematic review of frequency recognition algorithms for SSVEP over the past five years.
  • Categorization of algorithms based on learning paradigms: unsupervised, supervised, and deep learning.
  • Analysis of algorithm performance, strengths, and limitations.

Main Results:

  • Significant progress has been made across unsupervised, supervised, and deep learning methods for SSVEP frequency recognition.
  • Deep learning algorithms show particular promise for enhancing accuracy and robustness.
  • The review identifies key challenges and opportunities for future algorithm development.

Conclusions:

  • The field of SSVEP frequency recognition algorithms is rapidly evolving, driven by machine learning advancements.
  • Further research into novel deep learning architectures and hybrid approaches is warranted.
  • Optimizing algorithms will enhance the practical application and accessibility of BCI technology.