Assessment of mental workload based on multi-physiological signals

BACKGROUND: Mental workload is one of the contributing factors to human errors in road accidents or other potentially adverse incidents. OBJECTIVE: This research probes the effects of mental workload on the electroencephalographic (EEG) and electrocardiogram (ECG) of subjects in visual monitoring tasks, based on which a comprehensive evaluation model for mental workload is established effectively. METHODS: Three degrees of mental workload were obtained by monitoring tasks with different levels of difficulty. 20 healthy subjects were selected to take part in the research. RESULTS: The subjective scores showed a significant increase with the increase of task difficulty, meanwhile the reaction time (RT) increased and the accuracy decreased significantly, which proved the validity of three degrees of mental workload induced. For the EEG parameters, a significant decrease of [Formula: see text] energy was found in Frontal, Parietal and Occipital with the increase of level of mental workload, as well as a significant decrease of [Formula: see text] energy in Frontal, Central and Occipital, meanwhile a significant increase of [Formula: see text] energy occurred in Frontal and Occipital. There was a significant decrease of [Formula: see text] / [Formula: see text] in Occipital, and significant increases of [Formula: see text] / [Formula: see text] and ([Formula: see text] in Frontal, Central and Occipital, meanwhile ([Formula: see text] and WPE decreased significantly in Frontal and Occipital. Among the ECG parameters, it was shown that Mean RR, RMSSD, HF_norm and SampEn decreased significantly with the increase of task difficulty, while LF_norm and LF/HF showed significant increases. These EEG indictors in Occipital and ECG indictors were chosen and constituted a multidimensional original sample. Principal Component Analysis (PCA) was used to extract the principal elements and decreased the dimension of sample space in order to simplify the calculation, based on which an effective classification model with accuracy of 80% was achieved by support vector machine (SVM). CONCLUSION: This study demonstrates that the proposed algorithm can be applied to mental workload monitoring.