# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 4 Discussion
### Limitations  
Despite their advantages, machine learning-based analysis, simulation, and automation of maintenance and inspection processes face several limitations. One major limitation is the need for large, high-quality data sets to effectively train machine learning models; without sufficient data, models can produce inaccurate or unreliable results. Anotherchallenge is the complexity and cost associated with implementing and maintaining these systems, as they often require specialised knowledge and infrastructure. Machine learning models can also be difficult to interpret, leading to a 'black box' problem where it is unclear how decisions are made, which can hinder trust and adoption. Additionally, these systems can struggle to adapt in highly dynamic environments where conditions change rapidly, requiring the continuous retraining and updating of models. There is also the risk of overfitting, where models become too adapted to the training data and perform poorly on new, unseen data. Integrating machine learning into existing industrial processes can be complex, requiring significant time and resources to customise and integrate. In addition, machine learning models are susceptible to data errors, which can lead to skewed or unfair decision-making outcomes. Reliance on digital infrastructure and connectivity introduces cybersecurity risks, as these systems can be targeted for attacks [42; 43; 44; 45; 46; 47]. The ethical and regulatory implications of automating decisions and processes in critical industrial environments continue to evolve, creating potential legal and compliance challenges. With the current state of science and technology, there are several limitations in classifying the EEG signal using ML techniques:  
* Limited spatial resolution: EEG electrodes are typically placed on the scalp, providing limited spatial resolution compared to other neuroimaging techniques such as fMRI or MEG--this limitation makes it difficult to capture detailed spatial information about brain activity from the EEG signal into feature vectors/matrices for ML, which can affect the discriminative power of EEG-based ML models.
* Temporal variability of the EEG signal: it can exhibit significant temporal variability, both within and between individuals, resulting from attention, fatigue, and emotional states. This makes it difficult to develop unambiguous, reproducible and generalisable classification models.
* Noise and artefacts: muscle activity, eye blinks, electrode movement and environmental interference can significantly affect the quality of EEG signals, making it difficult for ML algorithms to accurately classify them.
* Limited training data: obtaining labelled EEG data sets to train ML models can be challenging due to the high cost and complexity of EEG data acquisition. Consequently, limited training data can lead to over-fitting or poor generalisation performance of ML models, especially for complex brain states or individual differences.
* Interpretability: ML models can achieve high classification accuracy, but interpreting the underlying neural mechanisms or features driving classification decisions remains a challenge--the need for a more complete understanding of how EEG features relate to specific cognitive processes or brain states to translate classification results into practical insights in real-world applications [48; 49; 50; 51].  
Circumventing or solving the above-mentioned limitations requires interdisciplinary research efforts, combining knowledge from medicine, signal processing, computer science, and biomedical engineering to develop reliable EEG classification methods. It seems that the fastest progress would come from the development of automatic data acquisition techniques, feature extraction methods and model interpretability [52; 53; 54; 55; 56].