Objective: This study investigates the feasibility of using a method based on electroencephalography (EEG) for deriving a driver’s mental workload index.
Background: The psychophysiological signals provide sensitive information for human functional states assessment in both laboratory and real-world settings and for building a new communication channel between driver and vehicle that allows for driver workload monitoring.
Methods: An experiment combining a lane-change task and n-back task was conducted. The task load levels were manipulated in two dimensions, driving task load and working memory load, with each containing three task load conditions.
Results: The frontal theta activity showed significant increases in the working memory load dimension, but differences were not found with the driving task load dimension. However, significant decreases in parietal alpha activity were found when the task load was increased in both dimensions. Task-related differences were also found. The driving task load contributed more to the changes in alpha power, whereas the working memory load contributed more to the changes in theta power. Additionally, these two task load dimensions caused significant interactive effects on both theta and alpha power.
Conclusion: These results indicate that EEG technology can provide sensitive information for driver workload detection even if the sensitivities of different EEG parameters tend to be task dependent.
Application: One potential future application of this study is to establish a general driver workload estimator that uses EEG signals.
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