Cross-subject mental workload classification using kernel spectral regression and transfer learning techniques

Abstract

Due to the poor generalizability of the subject-specific mental workload (MWL) classifier, we propose a cross-subject MWL recognition framework in this paper. Firstly, we use fuzzy mutual information-based wavelet-packet transform (FMI-WPT) technique to extract the salient physiological features of the MWL. Then, we combine kernel spectral regression (KSR) and transferable discriminative dimensionality reduction (TDDR) methods to reduce the dimensionality of the feature vector and to transfer the classifier model across subjects. Finally, the measured data analysis results are presented to show the enhanced performance of the proposed framework for multi-class MWL recognition.