Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy

Abstract

A brain-computer interface that measures the mental workload level of operators has applications in human-computer interactions (HCI) for reducing human error and improving work efficiency. In this study, concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were combined at the decision fusion stage for the classification of three mental workload levels induced by an n-back working-memory task. An average three-class classification accuracy of 42, 43, and 49% has been achieved across 13 participants for the fNIR-alone, EEG-alone, and EEG-fNIRS combined approach, respectively. The current study demonstrated a multimodality-based approach to decode human mental workload levels that may potentially be used for adaptive HCI applications.