Typical approaches for measuring mental workload are based on post-task surveys that heavily depend on the user’s final perception [1].

To address the issue, many computational frameworks are proposed to assess the user’s mental workload (MWL) in real-time through the data from multiple physiological modalities such as electrocardiogram (ECG), galvanic skin response (GSR), electroencephalogram (EEG), Functional near-infrared spectroscopy (fNIRS), and electromyography (EMG). This project aims to discriminate the surgeon’s intraoperative MWL levels accurately only based on the photoplethysmography (PPG), GSR, EMG and eyetracking data.