Applications of Deep Learning and Generative AI in Neuro-Medicine and Brain-Centric Interfaces

Dear Colleagues,

Advancements in artificial intelligence—particularly deep learning, transformers, and generative AI—are rapidly transforming the landscape of neuro-medicine and brain–computer interfaces (BCI). These technologies now offer powerful tools for interpreting complex neural and behavioral signals across a variety of sensing modalities, such as EEG, fNIRS, MRI, eye-tracking, EMG, and even emerging in-ear EEG devices. This Special Issue aims to bring together high-quality studies that explore the intersection of AI and neuro-medicine and promote novel solutions for cognitive health, neurological disorders, and brain–computer interfacing.

This Special Issue will focus on original research and reviews that explore the use of deep learning and generative AI in applications such as epilepsy detection, stroke prediction and rehabilitation, autism spectrum disorder (ASD) analysis, cognitive workload modeling, neurodevelopmental and neurodegenerative disorder assessments, and real-time or wearable BCI systems. We welcome submissions that apply advanced AI methods to multi-modal neural datasets or that propose innovative frameworks for low-channel EEG prediction, subject-transfer learning, or explainable decision-making in clinical settings.

We are particularly interested in work that includes both methodological innovation and translational relevance, including hybrid architectures, pre-trained vision/language models, LLMs in neuro-diagnostics, and privacy-preserving or real-time inference systems.

In this Special Issue, we welcome original research articles, reviews, and short communications. Research areas may include (but are not limited to) the following themes:

• Brain–computer interfaces and neurofeedback systems;
• Epilepsy and seizure prediction;
• Stroke detection and rehabilitation tracking;
• Autism and neurodevelopmental disorder diagnosis;
• Cognitive state and workload estimation;
• Explainable AI and model interpretability in neuro-medicine;
• Domain adaptation, transfer learning, and few-shot learning for neural signals;
• Low-resource and wearable neuro-sensing technologies.

We look forward to receiving your contributions.

Dr. Adham Atyabi
Guest Editor

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