# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 1 Introduction
An important element of these systems is the EEG (electroencephalographic) signal, captured using a device equipped with electrodes set in a specific hierarchy or arrangement in relation to the cortex of the brain. This signal represents the neuronal activity of a person using the BCI. The number of electrodes may vary from manufacturer to manufacturer [1; 2]. The distribution of electrodes on the head corresponds to a certain standard that covers significant areas; the medical standard for EEG signal acquisition is a distribution of '10-20', but there are devices with fewer electrodes, such as eight [3; 4]. An extensive study of brain activity is not needed for BCIs as we are not typically looking for abnormalities but rather are using them as control or communication devices, less often for early diagnosis. EEG-based BCIs represent the majority, hence the interest of researchers, engineers, and clinicians in developing methods and techniques for analysing them. The different brain rhythms captured on the EEG signal are characterised by differences in frequency and amplitude. The following rhythms are considered in the EEG signal itself: the delta rhythm (0.5-4 Hz), the theta rhythm (4-8 Hz), the alpha rhythm (8-12 Hz), the beta rhythm (12-35 Hz), and the gamma rhythm (above 35 Hz). To date, hundreds of algorithms have been identified for extracting EEG signal features and interpreting them (i.e., the identification of BCI user intent), especially when they are changed intentionally by the BCI user. Recent general advances in EEG classification (and BCI purposes specifically) have proved important for developing this group of solutions. EEG signal classification using ML techniques has many applications, some of which are listed below. * Monitoring central nervous system health and neurological disorders: These techniques can be used for the early detection and monitoring of neurological disorders such as Alzheimer's disease, Parkinson's disease, and stroke. Machine learning algorithms analyse EEG biomarkers associated with disease-specific changes in brain activity, providing non-invasive diagnostic tools and tracking disease progression and treatment progression. This provides early intervention, optimised treatment, and personalised healthcare management for people with neurological diseases. * Brain-computer interfaces: BCIs enable direct communication between the brain and external devices for early diagnosis, communication, and control, offering assistive technology solutions for people with mobility disabilities. Machine learning algorithms classify EEG signals associated with specific mental tasks or intentions, enabling users to control devices such as prosthetic limbs, computer cursors, or virtual keyboards through bioelectrical brain activity. * Neurofeedback: Neurofeedback uses real-time EEG signal classification to provide users with feedback on their brain activity patterns. Through presenting this feedback in a visual or audio format, individuals can learn to modulate brain activity and achieve their desired cognitive states such as relaxation, focus, or stress reduction. * Seizure detection and epilepsy monitoring: These techniques can facilitate the automated differentiation of normal brain activity from seizure-related EEG patterns, enabling early detection of epileptic seizures and providing timely warnings to patients or caregivers, increasing the safety and quality of life of people with epilepsy and facilitating rapid intervention and treatment. * Sleep stage classification: These techniques are essential for assessing sleep quality and diagnosing sleep disorders (insomnia, sleep apnoea, and others). Automated ML-based sleep stage classification systems provide valuable insights into sleep architecture and facilitate personalised treatment strategies to improve sleep health.