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Article

An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey

1
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
2
Science and Technology Development Corporation, Shenyang Ligong University, Shenyang 110159, China
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(14), 2767; https://doi.org/10.3390/electronics13142767
Submission received: 12 June 2024 / Revised: 2 July 2024 / Accepted: 12 July 2024 / Published: 14 July 2024
(This article belongs to the Special Issue EEG Analysis and Brain–Computer Interface (BCI) Technology)

Abstract

The brain–computer interface (BCI) is a direct communication channel between humans and machines that relies on the central nervous system. Neuroelectric signals are collected by placing electrodes, and after feature sampling and classification, they are converted into control signals to control external mechanical devices. BCIs based on steady-state visual evoked potential (SSVEP) have the advantages of high classification accuracy, fast information conduction rate, and relatively strong anti-interference ability, so they have been widely noticed and discussed. From k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) classification algorithms to the current deep learning classification algorithms based on neural networks, a wide variety of discussions and analyses have been conducted by numerous researchers. This article summarizes more than 60 SSVEP- and BCI-related articles published between 2015 and 2023, and provides an in-depth research and analysis of SSVEP-BCI. The survey in this article can save a lot of time for scholars in understanding the progress of SSVEP-BCI research and deep learning, and it is an important guide for designing and selecting SSVEP-BCI classification algorithms.
Keywords: BCI; SSVEP; classification algorithms; neural networks; deep learning BCI; SSVEP; classification algorithms; neural networks; deep learning

Share and Cite

MDPI and ACS Style

Wu, J.; Wang, J. An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey. Electronics 2024, 13, 2767. https://doi.org/10.3390/electronics13142767

AMA Style

Wu J, Wang J. An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey. Electronics. 2024; 13(14):2767. https://doi.org/10.3390/electronics13142767

Chicago/Turabian Style

Wu, Jiaxuan, and Jingjing Wang. 2024. "An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey" Electronics 13, no. 14: 2767. https://doi.org/10.3390/electronics13142767

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