Recognition of high-frequency steady-state visual evoked potential for brain-computer interface.
10.7507/1001-5515.202302034
- Author:
Ruixin LUO
1
;
Xinyi DOU
2
;
Xiaolin XIAO
1
;
Qiaoyi WU
1
;
Minpeng XU
1
;
Dong MING
1
Author Information
1. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China.
2. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Decoding algorithm;
High-frequency;
Steady-state visual evoked potential
- MeSH:
Humans;
Brain-Computer Interfaces;
Evoked Potentials, Visual;
Algorithms;
Discriminant Analysis;
Electroencephalography
- From:
Journal of Biomedical Engineering
2023;40(4):683-691
- CountryChina
- Language:Chinese
-
Abstract:
Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.
