Z-score linear discriminant analysis for EEG based brain-computer interfaces.
Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Rui
R
Zhang
Peng
P
Xu
Lanjin
L
Guo
Yangsong
Y
Zhang
Peiyang
P
Li
Dezhong
D
Yao
D000328:Adult; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D016002:Discriminant Analysis; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D055815:Young Adult