# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 1 Introduction
In [PERSON]'s disease, this involves analysing the EEG signal and looking for specific patterns. The signal is taken from three electrodes [7], pre-processed, and classified using SVM (support vector machine) classifiers, the KNN (K-nearest neighbours) method, and a random forest classifier. Methods have also been created to optimise filter use on the signal and image [7; 8; 9]. The flower pollination algorithm (FPA) has been used in this context. Signals are also processed by a recurrent neural network (RCNN) and MRI images as input, where the approach is extended by artificial intelligence methods with unsupervised learning, such as fuzzy clustering [5]. Research is also being conducted on EEG signals coordinated with speaking. Linear models are considered and compared with non-linear models using deep learning, and an internal database is used. Several solutions have been presented using different network models based on recursive layers, such as long short-term memory (LSTM) and generative adversarial networks (GANs) [10]. Research is emerging considering the use of EEG signals as BCIs in IoT-enabled systems. The techniques and effectiveness of signal processing using amplitude-shifting keys (ASKs) and frequency-shifting keys (FSKs) are being investigated. In these investigations, neural networks are considered specific encoders and decoders of transmitted wireless signals. Energy efficiency and data reconstruction fidelity in neural networks have also been accounted for [10]. It is also important to remove artefacts that arise from eye and muscle movement. To do so, researchers tend to use a deep learning (DL) model, referring to the collected and available signal before and after processing in the frequency domain. They use a spline filter or cascaded processing to de-noise this signal. The activation function is the tanh function [11]. Solutions are also being developed to de-noise any signal in real-time [12], as well as solutions that create multidimensional rows of data that are de-noised simultaneously to optimise the performance of the resulting model on large amounts of data [13]. Our proposed review of predictive maintenance focusing on the use of brain-computer interfaces (BCIs) brings novelty and significant contributions for several key reasons. BCIs introduce a new dimension, enabling direct communication between human operators and machines, improving decision-making in maintenance tasks within cyber-physical systems in Industry 5.0, where the human is at the centre of the system. This integration allows human expertise to be seamlessly combined with AI-based predictive maintenance systems, improving the interpretation of complex maintenance data. BCIs offer real-time, hands-free control and monitoring, which is particularly beneficial in environments where manual intervention is difficult or dangerous. This review sheds light on how human cognitive feedback can improve predictive models by identifying subtle machine behaviours that artificial intelligence alone can overlook. BCIs also enable faster response times in critical PdM scenarios by allowing operators to intervene directly based on mental commands. How the human brain's ability to recognise patterns can complement AI, leading to more accurate and proactive maintenance strategies, is currently being investigated. The review highlights novel applications of BCIs, such as immersive virtual environments for remote maintenance and training. It discusses the challenges of integrating BCIs in industrial environments, such as signal accuracy, and suggests potential solutions. From this point of view, this review provides a fresh perspective on human-machine collaboration, making it different from other predictive maintenance reviews.