A robust principal component analysis algorithm for EEG-based vigilance estimation

Feature dimensionality reduction methods with robustness have a great significance for making better use of EEG data, since EEG features are usually high-dimensional and contain a lot of noise. In this paper, a robust principal component analysis (PCA) algorithm is introduced to reduce the dimension of EEG features for vigilance estimation. The performance is compared with that of standard PCA, L1-norm PCA, sparse PCA, and robust PCA in feature dimension reduction on an EEG data set of twenty-three subjects. To evaluate the performance of these algorithms, smoothed differential entropy features are used as the vigilance related EEG features. Experimental results demonstrate that the robustness and performance of robust PCA are better than other algorithms for both off-line and on-line vigilance estimation. The average RMSE (root mean square errors) of vigilance estimation was 0.158 when robust PCA was applied to reduce the dimensionality of features, while the average RMSE was 0.172 when standard PCA was used in the same task.