EOG feature relevance determination for microsleep detection

Abstract

Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learning vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 -4.9 % and 1.9 -10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 -0.006 % and 0.002 -0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6  1.3 % and 92.3  0.2 % for GRLVQ and SVM, respectively. GRLVQ permits objective feature reduction by inclusion of all processing stages, but is not as accurate as SVM.