Machine learning and clinical EEG data for multiple sclerosis: A systematic review.

Area of Science:

• Neuroscience and Artificial Intelligence
• Biomedical Engineering
• Clinical Neurology

Background:

• Multiple Sclerosis (MS) is a chronic neuroinflammatory disease affecting the Central Nervous System (CNS), characterized by immune-mediated myelin sheath destruction.
• Effective prediction, diagnosis, monitoring, and treatment (PDMT) are crucial for managing MS and improving patient outcomes.
• Electroencephalography (EEG) combined with machine learning (ML) and deep learning (DL) presents novel opportunities for MS management.

Purpose of the Study:

• To systematically review the application of ML and DL models using EEG data for Multiple Sclerosis (MS) management.
• To explore methodologies, focusing on DL architectures and recent advancements in ML and EEG technologies.
• To address challenges, biases, and mitigation strategies in ML-based EEG analysis for MS.

Main Methods:

• Systematic review of existing research on ML and DL models applied to EEG data for MS.
• Focus on methodologies including Convolutional Neural Networks (CNNs) and hybrid DL models.
• Analysis of ML techniques, EEG advancements, preprocessing, dataset diversity, cross-validation, and explainable AI (AI).

Main Results:

• ML and DL models applied to EEG data have shown significant improvements in MS diagnosis and monitoring.
• Recent advancements in ML techniques and EEG technologies enhance the accuracy and efficacy of MS assessment.
• Various strategies, including advanced preprocessing and explainable AI, can mitigate challenges and biases in ML-based EEG analysis.

Conclusions:

• ML-enhanced EEG analysis holds transformative potential for improving the prediction, diagnosis, monitoring, and treatment of MS.
• Future research should focus on overcoming existing limitations to further refine clinical practice.
• Continued development in ML algorithms and EEG technology integration is key to advancing MS care.