A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application

TL;DR

This systematic review examines how combining multimodal EEG data with machine learning enhances clinical diagnosis accuracy across various neurological and psychiatric conditions.

Contribution

It provides a comprehensive overview of recent studies applying multimodal EEG data in ML models for clinical applications, highlighting data fusion strategies and performance improvements.

Findings

Multimodal EEG data improves model accuracy in clinical tasks.

Support vector machines and decision trees are commonly used.

Most studies report accuracy gains with multimodal approaches.

Abstract

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to enhance the accuracy of ML and DL models. Combining EEG with other modalities can improve clinical decision-making by addressing complex tasks in clinical populations. This systematic literature review explores the use of multimodal EEG data in ML and DL models for clinical applications. A comprehensive search was conducted across PubMed, Web of Science, and Google Scholar, yielding 16 relevant studies after three rounds of filtering. These studies demonstrate the application of multimodal EEG data in addressing clinical challenges, including neuropsychiatric disorders, neurological conditions (e.g., seizure detection), neurodevelopmental disorders (e.g., autism spectrum disorder), and sleep stage classification. Data fusion occurred at three levels: signal, feature, and decision levels. The most commonly used ML models were support vector machines (SVM) and decision trees. Notably, 11 out of the 16 studies reported improvements in model accuracy with multimodal EEG data. This review highlights the potential of multimodal EEG-based ML models in enhancing clinical diagnostics and problem-solving.