Using Multimodal Methods and Machine Learning to Recognize Mental Workload: Distinguishing Between Underload, Moderate Load, and Overload

Mental workload recognition is of great significance in preventing human errors and accidents. This study constructed a multimodal recognition scheme to recognize three mental workload states: underload, moderate load, and overload. Based on driving scenarios, these three states were induced in this study by changing the driving modes and situations. Multimodal recognition of underload, moderate load, and overload was performed using electroencephalography (EEG), electrocardiography (ECG), and pupillometry. In addition, various machine learning methods were used to evaluate the recognition performance of different feature combinations. The results showed that the random forest method, trained using spectral power, pupil diameter, and heart rate variability, achieved the highest recognition accuracy of 83.13% for the three mental workload states. This study provides valuable reference information for multimodal recognition of mental workload states.