Automatic Discrimination of Task Difficulty Predicted by Frontal EEG Activity During Working Memory Tasks in Young and Elderly Drivers

It is desirable to prevent traffic accidents by focusing on elderly people’s brain characteristics. The attention level during driving depends on the amount of information-processing resources. This study first aimed at investigating the effects of the change in attention levels on the electroencephalogram (EEG) waves during the graded working memory tasks for a traffic situation. With the increase in memory loads, reaction times were delayed in the elderly than the young group. The difficult tasks activated the induced δ and θ powers in the frontal midline area primarily in the elderly, during the selective task for a target. The elderly could retain the attention level because of the activated slow EEG responses, regardless of the task performance, although the increased δ wave may reflect drowsiness. Because the assistance system based on drivers’ brain signals can prevent car accidents, this study also aimed at evaluating the analytical method to automatically discriminate the different attentional tasks from the EEG signals. Compared with k-nearest neighbors and artificial neural networks, support vector machines more accurately classified attention levels (i.e., task difficulty) during working memory tasks reflecting a change in the induced δ and θ waves. This result can be related to a brain-computer interface system to judge the task difficulty during driving and alert a driver to danger. The experimental tasks for this study were limited because they involved simulations only in which participants recognized guided boards and removed irrelevant information. Real-time judgments should be investigated using EEG data to improve systems that can alert drivers to oncoming dangers.