# Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review

## TL;DR

This review explores how AI improves EEG analysis for neurological and eye movement disorders, highlighting recent methods and challenges.

## Contribution

A systematic review of AI techniques in EEG analysis for neurological and oculomotor disorders over the past decade.

## Key findings

- AI models, including deep learning, show promise in analyzing EEG data for neurological and oculomotor disorders.
- Common challenges include small sample sizes and heterogeneous datasets.
- Standardized methodologies and larger datasets are needed for clinical translation.

## Abstract

Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis for the diagnosis, classification, and monitoring of neurological conditions, including oculomotor-related disorders. Following the PRISMA guidelines, a structured literature search was conducted across major scientific databases, resulting in the inclusion of 15 peer-reviewed studies published over the last decade. The reviewed works encompass a range of neurological and ocular-related disorders and employ diverse AI models, from conventional ML algorithms to advanced DL architectures capable of learning complex spatiotemporal representations of neural signals. Key trends in feature extraction, signal representation, model design, and validation strategies are synthesized here to highlight methodological advancements and common challenges. While the reviewed studies demonstrate the growing potential of AI-enhanced EEG analysis for supporting clinical decision-making, limitations such as small sample sizes, heterogeneous datasets, and limited external validation remain prevalent. Addressing these challenges through standardized methodologies, larger multi-center datasets, and robust validation frameworks will be essential for translating EEG-driven AI approaches into reliable clinical applications. Overall, this review provides a comprehensive overview of current methodologies and future directions for AI-driven EEG analysis in neurological and oculomotor disorder assessment.