Classification algorithms of error-related potentials in brain-computer interface.
10.7507/1001-5515.202012013
- Author:
Jinsong SUN
1
;
Tzyy-Ping JUNG
1
;
Xiaolin XIAO
1
;
Jiayuan MENG
1
;
Minpeng XU
1
;
Dong MING
1
Author Information
1. Academy of Medical Engineering and Translational Medicine. Tianjin University, Tianjin 300072, P.R.China.
- Publication Type:Journal Article
- Keywords:
brain-computer interface;
error-related potentials;
pattern recognition;
single trial recognition
- MeSH:
Algorithms;
Brain;
Brain-Computer Interfaces;
Discriminant Analysis;
Electroencephalography;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2021;38(3):463-472
- CountryChina
- Language:Chinese
-
Abstract:
Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.
