A review of researches on decoding algorithms of steady-state visual evoked potentials.
10.7507/1001-5515.202111066
- Author:
Man YANG
1
;
Tzyy-Ping JUNG
1
;
Jin HAN
2
;
Minpeng XU
1
;
Dong MING
1
Author Information
1. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.
2. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China.
- Publication Type:Review
- Keywords:
Brain-computer interface;
Decoding algorithm;
Feature extraction;
Pattern recognition;
Steady-state visual evoked potentials
- MeSH:
Algorithms;
Brain-Computer Interfaces;
Electroencephalography;
Evoked Potentials, Visual;
Photic Stimulation
- From:
Journal of Biomedical Engineering
2022;39(2):416-425
- CountryChina
- Language:Chinese
-
Abstract:
Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.
