Mental Workload Recognition by Combining Wavelet Packet Transform and Kernel Spectral Regression Techniques

A Mental Workload (MWL) recognition system was developed based on psychophysiological data to assess temporal variations in MWL levels. Salient EEG features were first extracted by using fuzzy mutual-information-based wavelet-packet transform (FMI-WPT). Then we adopted the kernel spectral regression linear discriminant analysis (KSRDA) to reduce the EEG feature dimensionality and to simultaneously enhance the inter-class discrimination capacity of the MWL classifiers. By combining FMIWPT and KSRDA techniques, we designed, evaluated and compared different types of MWL classifiers. The results demonstrated a improvement of the MWL classification accuracy by the proposed feature reduction method and classifier design framework. Particularly, it was shown by extensive comparative studies that the KNN and SVM outperform other classifiers.