Abstract
Measuring mental fatigue is essential in assessing the performance of those subjects whose careers involve severe mental activity. Recently, many analytical methods have been applied to electroencephalograms (EEGs) in order to quantitatively detect the fatigue state, but their accuracy is still not satisfactory. Factorization methods have been employed in our study to extract fatigue-related features from information captured from the ongoing raw EEG signals. The EEG signals were recorded from 32 channels from 17 healthy subjects before and after 3 h of severe mental activity. After preprocessing the raw EEGs, it was arranged in matrices to be decomposed by non-negative methods named NMF, LNMF, SNMF, DNMF, NTF, and DNTF. A comparative study of the methods was carried out by using support vector machine (SVM) (Sameni et al. in IEEE Trans Signal Process 58:2363-2374, 2010; Kadirgama et al. in Arab J Sci Eng 37:2269-2275, 2012) with extracted discriminative subspaces in order to classify raw EEGs into two "mental states" (fatigued/not fatigued). Experimental results demonstrated that discriminant DNTF outperformed (p < 0.05) the other compared non-negative methods in terms of accuracy, feature storage, and robustness.