ESTIMATION OF COGNITIVE WORKLOAD BY APPROXIMATE ENTROPY OF EEG

The traditional cockpit display-control system usually has great many instruments and much complex information, which leads to the pilots to take a long time to be familiar with the cockpit interface and often cause accidents when emergencies happen. Thus it is necessary to evaluate the cognitive workload of the pilots under multitask conditions. A simplified evaluation method of cognitive workload by approximate entropy (ApEn) of electroencephalography (EEG) is proposed in this paper. We design a series of experiments about the flight instruments, which have different instrument number, pointer speed, and operation difficulty, and collect the EEG, interval time (IT), and misjudgment rate (MR), then classify and analyze these data with ApEn algorithm, traceability, and dualistic linear regression method. It can be found that ApEn is increased with increasing experiment difficulty, which shows that ApEn can be used as the evaluation criteria of cognitive workload. As the ApEn and the number of dipoles have a positive correlation relationship, the cognitive workload and ApEn are both changed with increasing the number of brain dipoles. Taking MR and IT as the independent variables, and ApEn as the dependent variable, we obtain an empirical formula to simplify the assessment process of the cognitive workload. This study concludes that ApEn can be used as the evaluation criteria of cognitive workload, which could be applied in the ergonomics estimation of human-interface interaction field.

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