Neuro-physiological emotional profiling model for mental fatigue

Abstract

Mental fatigue is one of the critical issues in this world that affects the overall well being of people in terms of cognitive performance, emotions, and decision making. Mental fatigue is one of the typical human infirmities. Studies report that sleep deprivation and long work hours increase the likelihood of fatigue and fatigue related accidents, and contribute to reduced productivity, errors, and accidents. Fatigue is one of the biggest causes of crashes involving cars, lorries, and buses. Traditionally, mental fatigue has been assessed through subjective methods such as interviews and psychometric questionnaires, which are prone to bias, inconsistency, and imprecision. Failure to address mental fatigue effectively may lead to impaired decision making, reduced safety in transportation and healthcare, and overall decline in quality of life. This research aims to address these gaps by proposing the Neuro Physiological Emotional Profiling Model for Mental Fatigue (NPEMMF), which integrates electroencephalography (EEG) data with human emotional stimuli to provide a more objective and reliable assessment of mental fatigue. Specifically, the objectives are: (i) to identify the relationship between mental fatigue and its consequences on human emotion, (ii) to develop a neurophysiological emotional profiling model for mental fatigue based on ERP features, and (iii) to evaluate the performance of the neurophysiological profiling model based on the affective space model for detecting the underlying emotions in mental fatigue. Event Related Potential (ERP) was chosen due to its sensitivity in capturing time locked brain responses to emotional stimuli, while the Wide Neural Network (WNN) was used as the classifier to analyze the gathered data to find trends and create a model for mental fatigue due to its robustness in handling nonlinear and high dimensional EEG data. The International Affective Picture System (IAPS) was used as a stimulus instrument for emotions such as happy, calm, fear, and sad. Experimental results indicated that the peak to peak data produced more reliable and consistent results compared to peak to peak and latency data. EEG channel analysis according to the affective space model revealed that for positive arousal, the frontal EEG channels of F3 and F4 are most appropriate for studying emotions happy and fear. On the other hand, the EEG channel Cz was suitable for studying emotions with negative arousal. The ERP components that are most suitable for positive valence are N1, P1, N2, P2, N3, and P3. For negative valence, the most suitable ERP components are the Late Positive Potentials (LPP). The evaluation confirmed that the NPEMMF framework reduces bias and inconsistency compared to traditional subjective approaches, providing a more objective and accurate profiling of mental fatigue. Overall, this research contributes to advancing knowledge in mental fatigue assessment and emotional profiling. The proposed framework has potential applications in high risk domains, such as the transportation sector, where reliable detection and regulation of mental fatigue can enhance safety and decision making.