# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 1 Introduction
* Cognitive state monitoring: ML algorithms analyse EEG functions related to attention, memory, and cognitive load, providing real-time feedback on users' cognitive performance and mental states to help optimise task allocation, increase human performance, and prevent errors related to cognitive fatigue in people performing particularly important functions (e.g., pilots, flight controllers). * Emotion recognition: EEG signals can classify signals associated with different emotional states, such as happiness, sadness, fear, and anger, for affective computing, human-computer interactions, and psychological research [1; 2; 3; 4]. These applications demonstrate the versatility and potential impact of EEG signal classification using machine learning techniques in areas ranging from healthcare and rehabilitation to human-computer interaction and signal processing research. This demonstrates the novelty and contribution of this study: continued research and innovation hold the promise of better understanding brain function, developing innovative solutions for education and entertainment, and improving human health and wellbeing. EEG signals have been studied for 100 years, but new correlations are constantly being discovered; including, for example, simultaneous EEG-fMRI studies that bridge EEG defects. EEG signals play a key role in BCIs by capturing electrical brain activity, allowing users to interact with devices without physical movement. In hands-free control, BCIs enable operators to manage complex systems by providing a non-invasive method of controlling machines or equipment using brain signals alone. These technologies impact predictive maintenance by enabling the early detection of equipment anomalies, as operators can react faster with hands-free systems. BCIs increase the efficiency of monitoring tasks in industrial settings through the improvement of maintenance response times. Predictive maintenance can be further optimised by integrating BCI-based insights with machine learning models that predict failures or maintenance needs. As a result, BCI can significantly reduce downtime and operational costs through improving real-time problem detection and correction. The topic under discussion is not popular or frequently reported in the literature: a review of six major bibliographic databases (PubMed, WoS, Scopus, DBLP, Cochrane, and EBSCO) with the keywords 'EEG classification' and related words yielded 464 publications (1956-2024), including 12 reviews (Figure 4). However, the publication growth rate is significant: 251 of these were published in the last 5 years (54.09%), and in the last 10 years, 353 (76.08%). Articles concerning 'EEG' and 'classification' as separate words numbered 12,354. A similar literature review with the keywords 'EEG classification', 'artificial intelligence/AI', and related words yielded only 39 publications (2005-2024, Figure 5). Another literature review with the keywords 'EEG classification','machine learning/ML', and related words yielded 92 publications (2011-2024, Figure 6). These results indicate that there is still much to be explored in the topic of EEG signal classification using machine learning and that this topic is a developmental one which is worthy of further research. This study aims to investigate the comparative performance of EEG signal classification using different neural network architectures and different learning methods. The novelty lies in comparing them with an actual EEG signal used for a specific purpose (BCI-mediated control) with this application in mind, as well as contributing to new methods for their use with minimal handling, promoting the automation of EEG signal use procedures (e.g., in Industries 4.0/5.0) through the use of hands-free technology. Research on detecting disease anomalies in EEG signals using artificial intelligence methods has been ongoing for many years. Research on Alzheimer's disease, dementia, and mild cognitive impairment is emerging [5; 6; 7].