Key result
The weighted-normalized mutual information feature selection (W-NMIFS) method improved mental fatigue classification precision compared with mRMR, NMIFS, and N-NMIFS algorithms.
Population
10 volunteers performing 3 different mental tasks designed to introduce mental fatigue in low, medium and…
Comparison
Weighted-normalized mutual information feature… vs mRMR, NMIFS and N-NMIFS algorithms
Authors
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W-NMIFS boosts EEG mental fatigue classification precision; leaves open validation for cardiovascular monitoring.
The proposed W-NMIFS method improves EEG feature selection for mental fatigue classification compared to existing algorithms.
Zhang et al. (2016) studied 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.
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