Progresses and prospects on frequency recognition methods for steady-state visual evoked potential.
10.7507/1001-5515.202102031
- Author:
Yangsong ZHANG
1
;
Min XIA
1
;
Ke CHEN
2
;
Peng XU
2
;
Dezhong YAO
2
Author Information
1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan 621010, P. R. China.
2. MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.
- Publication Type:Review
- Keywords:
Deep learning;
Electroencephalogram;
Frequency recognition;
Machine learning;
Steady-state visual evoked potential
- MeSH:
Algorithms;
Brain-Computer Interfaces;
Electroencephalography/methods*;
Evoked Potentials, Visual;
Photic Stimulation
- From:
Journal of Biomedical Engineering
2022;39(1):192-197
- CountryChina
- Language:Chinese
-
Abstract:
Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.
