Electroencephalography: Advances in Clinical Applications

Dear Colleagues,

Electroencephalography remains one of the most accessible and widely used neurophysiological tools in clinical practice. Yet, the translation of computational methods to bedside EEG interpretation has lagged behind other domains. At the same time, artificial intelligence and machine learning have transformed clinical decision-making in radiology, pathology, and cardiology, achieving diagnostic performance that rivals expert clinicians. This gap persists despite rapid advances in research settings, where modern architectures, including convolutional networks, autoencoders and transformers, have demonstrated strong performance on EEG decoding tasks. Key barriers include limited data standardization across clinical sites, poor model generalizability to unseen patients, and the absence of clear regulatory and validation pathways for modern, computer-assisted EEG tools.

This Special Issue welcomes original research articles and reviews that advance EEG-based diagnosis, prognosis, or treatment across neurological and psychiatric conditions. Particularly, it aims to bridge the gap between computational EEG research and clinical application. Contributions leveraging computational, machine learning and AI methods are of particular interest, though studies advancing traditional EEG methodology with demonstrated clinical impact are equally encouraged. We especially welcome work that benchmarks automated approaches against expert clinical interpretation or that addresses the challenges of deploying these tools in real-world clinical environments.

Topics of interest for publication include, but are not limited to, the following:

• Automated EEG interpretation and clinical diagnosis;
• Machine learning architectures for seizure detection, prediction, and classification;
• Sleep EEG biomarkers for neurological and psychiatric conditions;
• EEG-based assessment of consciousness in clinical populations;
• Computational approaches to neurodegenerative disease detection;
• Multimodal integration of EEG with neuroimaging, neurochemistry, or wearable biosensors;
• Brain–computer interfaces for rehabilitation;
• Signal processing and standardization pipelines for clinical EEG;
• Benchmarking AI performance against expert clinical judgment.

Dr. Leonardo S. Barbosa
Guest Editor

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