Investigation of mental fatigue through EEG signal processing based on nonlinear analysis: Symbolic dynamics

To investigate nonlinear analysis of attention physiological indices this study used a simple repetitive attentive task in four consecutive trials that resulted in mental fatigue. Traditional performance indices, such as reaction time, error responses, and EEG signals, were simultaneously recorded to evaluate differences between the trials. Performance indices analysis demonstrated that a selected task leads to mental fatigue. In addition, the study aimed to find a method to determine mental fatigue based on nonlinear analysis of EEG signals. Symbolic dynamics was selected as a qualitative method used to extract some quantitative qualifiers such as entropy. This method was executed on the reaction time of responses, and EEG signals to distinguish mental states. The results revealed that nonlinear analysis of reaction time, and EEG signals of the frontal and central lobes of the brain could differentiate between attention, and occurrence of mental fatigue in trials. In addition, the trend of entropy variation displayed a reduction in the complexity of mental activity as fatigue occurred.

► Mental fatigue indices’ variation discussed during simple long-term attentive task.
► Symbolic dynamics of reaction time and EEG signal determine mental state variation.
► Nonlinear quantifiers such as entropy can display chaotic behaviors of the brain.
► Frontal and central lobes of the brain are effective in attention investigations.
► Mental fatigue causes a reduction in the complexity of the brain’s activity.