Sequential Nonlinear Encoding; A Low Dimensional Regression Algorithm with Application to EEG based Driving Fatigue Detection

Abstract

Background: Regression analysis of real world data has not always been an easy task, especially when input vectors are presented in a very low dimensional space. EEG based fatigue detection is one such low dimensional problems, playing a major role in reducing the risk of fatal accidents. New Method: We introduce a novel Regularized Kernel Projection Pursuit Regression method which is a two-step nonlinearity encoding algorithm tailored for such very low dimensional problems as fatigue detection. This way, the data nonlinearity can be investigated from two different perspectives, first by transforming the data into a high dimensional intermediate space and then by using their spline estimations to the output variables which allows for a hierarchical unfolding of data. Result: Experimental results on the SEED VIS database shows an average RMSE value of 0.1080% and 0.1054% respectively for the temporal and posterior areas of the brain. Our method is also validated by conducting some experiments on Parkinson's disease prediction which further demonstrate the efficiency of our method. Conclusion: This paper proposes a novel regression algorithm to address the encoding problem of a highly complex low dimensional data, which is usually encountered in bio-neurological prediction tasks like EEG based driving fatigue detection.