EEG and Artificial Intelligence Across Two Domains: A Narrative Review of Clinical Diagnosis of Neurological Disorders and Brain-Computer Interfaces
Electroencephalography (EEG) underpins two complementary lines of research: the clinical diagnosis of neurological disorders and the design of Brain–Computer Interfaces (BCIs) for communication and control. Despite shared signal–processing foundations, the two fields impose very different technical and validation requirements. This narrative review synthesises recent advances (2018–2025) across both domains, distinguishing laboratory performance from clinical readiness throughout. For clinical diagnosis, deep-learning models (convolutional, recurrent, graph-based, and transformer architectures) have reported offline, subject-dependent accuracies above 90% for Alzheimer's disease, Parkinson's disease, and epilepsy. We interpret these figures with caution: most were obtained on small, curated datasets under offline protocols, and should therefore be viewed as candidate computational biomarkers rather than clinically validated ones. For BCIs, we discuss realistic ranges of motor-imagery accuracy (typically 70–85% cross-subject, offline) and SSVEP information transfer rates (up to approximately 100 bits/min under controlled laboratory conditions).We compare CNNs, LSTMs, Transformers, and Graph Neural Networks in terms of data requirements, interpretability, robustness, and suitability for clinical deployment, separating conceptual similarity between diagnostic and BCI pipelines from genuine operational transferability. Human factors that AI alone cannot resolve — BCI illiteracy (15-30% of users), calibration burden, inter-subject variability, and cognitive fatigue — are discussed as fundamental limitations. Finally, we review emerging directions (hybrid BCIs, explainable AI, federated learning) and regulatory frameworks such as the EU AI Act and GDPR as components of a realistic pathway toward trustworthy EEG systems, recognising that clinical deployment remains a long-term goal requiring independent validation, regulatory approval, and prospective clinical trials.