Diagnosis, Prediction, and Treatment of Neuropsychological Diseases: Application of EEG-Based Technology

Dear Colleagues,

Electroencephalography (EEG) is increasingly recognized as a practical and scalable technology for the diagnosis, prediction, and monitoring of treatment outcomes in neuropsychological diseases. However, significant gaps remain, including limited generalizability across cohorts and devices, small and heterogeneous datasets, insufficient interpretability for clinical trust, and persistent barriers to real-world translation—such as challenges related to artifact robustness, reproducibility, and prospective validation.

The Special Issue, “Diagnosis, Prediction, and Treatment of Neuropsychological Diseases: Application of EEG-Based Technology”, aims to consolidate methodological and clinical advances that directly address these challenges. We particularly welcome contributions that reflect current trends, including self-supervised and foundation EEG models, transformer- and state-space-based approaches for long recordings, multimodal fusion (e.g., EEG combined with imaging, physiology, or digital phenotyping), and deployment-oriented pipelines for wearable or home-based monitoring.

The scope includes (but is not limited to) EEG biomarkers for neuropsychological disorders; automated detection and prognostic modeling; individualized and closed-loop paradigms (e.g., neurofeedback/BCI); treatment-response tracking; artifact handling and quality control; explainable and uncertainty-aware AI; benchmarking on open datasets; and clinically meaningful validation strategies.

We invite original research, systematic reviews, and perspectives that advance reproducible and clinically actionable EEG-based technologies and accelerate the translation of laboratory findings into tangible patient benefit.

Dr. Agnieszka Wosiak
Guest Editor

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