Influences of Information Processing Modality on Mental Workload Recognition Performance Based on EEG Feature Extraction

Abstract

Accurate recognition of mental workload is significant for optimizing the human-machine interaction and avoiding the regrade of task performance levels due to overloading or underloading of mental workload. In past studies, the use of Electroencephalogram (EEG) signals has shown high performance in the recognition of operators' mental workload levels, however, most of the studies were conducted using a single visual modality task or dual visual modality tasks. But in real-world operational tasks, auditory-visual modalities tasks are commonly involved, and there have been relatively few researches on the EEG recognition of mental workload levels in auditory-visual modalities tasks. Therefore, in this research, visual single modality task scenario and audio-visual dual-modality task scenario were set up based on simulated flight experiments. For each task scenario, two levels of mental workload were induced by the differences in task complexity. Twenty subjects were recruited and their NASA-TLX scales and EEG signals were collected during the experiment. Two types of feature extraction methods were used, including Power Spectral Density (PSD) and Common Spatial Pattern (CSP), to recognize the mental workload levels. The research results indicated that the information processing modality did not have a significant influence on the performance of recognition for mental workload based on EEG feature extraction.