Prototype interface for detecting mental fatigue with EEG and XAI frameworks in Industry 4.0

Abstract

Mental fatigue correlates to prolonged cognitive activity. It stresses the brain of so many ideas or thoughts that can translate into commitments, jobs, and to-do at home - leaving a person exhausted and hindering productivity and overall cognitive function. Moreover, extracranial electroencephalogram (EEG) signals are an excellent indicator of the brain conditions of a person. Besides, mental fatigue increases power in frontal theta (θ) and parietal alpha (α) EEG rhythms. On the other hand, artificial intelligence (AI) and EEG signals improved the classification and regression results in different applications with new convolutional neural networks (CNNs), including EEGNet. Some of these results in the literature are applications for disabled persons, detection of mental fatigue driving, mental workload, and schizophrenia. Despite the benefits of applying CNNs to interpret EEG signals, the final products' applications are still limited due to the expertise required for working with this model. Alternatively, explainable AI (XAI) refers to the principle of AI operation and the presentation of the results obtained in the most user-friendly way possible. Explainable models must provide a clear description of their results without having to forget high learning efficiency. It must also be possible for users to understand the emerging generation of artificial intelligence mechanisms, place a certain degree of trust in it, and work with it and manage it efficiently. The present chapter proposes a new application that brings the advantages of using EEG signals together with the EEGNet structure, adding explainable intelligent models simplifying the detection of mental fatigue and preventing accidents in Industry 4.0. A study of various activities as a stimulus under a workstation scenario is analyzed to determine criteria associated with preventing accidents in the physical plant of an industrial building. Typically, it is difficult for the device to provide us with high-quality signals; there are invasive systems that allow greater precision. In this project, we use a non-invasive device for this purpose.