In recent years, mechanisms to detect and correct human errors by AI and efforts to automate business operations have been advancing. Human errors are expected to be fatal in the future, and they need to be predicted and prevented in advance. Researches has also been proposed to analyze human errors from a model of human behavior and electroencephalograms, but no other useful biological information is considered. Therefore, in this research, we thought that prediction and detection could be performed by adding autonomic nerves that can be acquired from heart rate as biological information and observing patterns before and after mistakes. In order to realize it, we measured the pulse and EEG of the worker who is carrying out the computational task, developed an experimental system to investigate the question and timing of the task, assumed that it that it is possible to evaluate the electroencephalogram and pulse at the time of human error occurrence by the computational task. In addition, a questionnaire based on NASA Task Load Index was conducted to enable analysis using subjective assessment of the tasks. Through the evaluation experiments, it was suggested that it is possible to detect the occurrence of human error in the group that answered that emphasized a particular measure in the questionnaire.