Mental Workload Classification Based on Semi-Supervised Extreme Learning Machine

The real-time operator's mental workload (MWL) monitoring system is crucial for the design and development of adaptive operator-aiding/assistance systems. Although the data-driven approach has shown promising performance for MWL recognition, its major challenge lies in the difficulty in acquiring extensive labeled data. This paper attempts to apply the semi-supervised extreme learning machine (ELM) to the challenging problem of operator's mental workload classification based only on a small number of labeled physiological data. The real data analysis results show that the semi-supervised ELM method can effectively improve the accuracy and computational efficiency of the MWL pattern classification.