The weighted-normalized mutual information feature selection (W-NMIFS) method improved mental fatigue classification precision compared with mRMR, NMIFS, and N-NMIFS algorithms.
The proposed W-NMIFS method improves EEG feature selection for mental fatigue classification compared to existing algorithms.
Analyzing EEG (electroencephalograph) signal is one of the most important methods of mental fatigue classification. Previous studies mainly describe the EEG features they use, however, the process of selecting features has not been synthetically researched. This study proposes the weighted-normalized mutual information feature selection (W-NMIFS) method to select features. W-NMIFS criterion optimizes the estimation of redundancy information in mutual information algorithm. Considering general features which are used in EEG analysis, several important ones are selected by W-NMIFS criterion. In order to guarantee the rigor of the experiment, the source datasets are collected from 10 volunteers using 10-20 EEG system. 3 different mental tasks are designed to introduce mental fatigue in low, medium and high levels. LDA (Linear discriminant analysis) classifier is used to assess mental fatigue and compare different feature selection algorithms. The results of mental fatigue classification show that the chosen features using W-NMIFS criterion are suitable for LDA algorithm. The classification precision is improved compared with mRMR (minimal-redundancy-maximal-relevance), NMIFS (normalized mutual information feature selection) and N-NMIFS (novel normalized mutual information feature selection) algorithm.
Zhang et al. (Sun,) conducted a other in Mental fatigue (n=10). Weighted-normalized mutual information feature selection (W-NMIFS) vs. mRMR, NMIFS, and N-NMIFS algorithms was evaluated on Mental fatigue classification precision. The weighted-normalized mutual information feature selection (W-NMIFS) method improved mental fatigue classification precision compared with mRMR, NMIFS, and N-NMIFS algorithms.