Advances in Biomedical Engineering and Artificial Intelligence for Neurological Health

Dear Colleagues,

The convergence between artificial intelligence and biomedical engineering is ushering in a new era in healthcare, especially in the study, diagnosis, treatment, and rehabilitation of nervous system disorders. As neurological conditions continue to rise globally in both prevalence and complexity, there is an urgent need to develop more accurate, intelligent, and personalized tools that enable efficient and proactive clinical approaches.

Artificial intelligence has become a key catalyst to accelerate these advances. From automated processing of neural signals (EEG, MEG, EMG, and NIRS) to advanced neuroimaging analysis (MRI, fMRI, and PET) and the application of predictive models for neurodegenerative diseases, AI is redefining the boundaries of what is possible in clinical neuroscience and neuroengineering.

Brain–computer interfaces, clinical decision support systems, neuroprosthetics, intelligent neurorehabilitation, neural data simulation through generative models, and algorithm-guided bioprinting are just a few of the innovations transforming the landscape.

This Special Issue aims to gather cutting-edge research that uses AI techniques in biomedical applications oriented toward the nervous system. We welcome original studies, systematic reviews, technological developments, and methodological proposals that offer innovative solutions with real impact in the clinical and/or experimental field.

This Special Issue is aimed at researchers, developers, and professionals in artificial intelligence, biomedical engineering, neuroscience, medical informatics, and related areas.

Some AI techniques (not excluding related themes) focusing on neuroscience topics include the following:

• Machine learning for the diagnosis of neurological diseases (Alzheimer's, Parkinson's, epilepsy, etc.);
• Deep learning applied to neuroimaging (MRI, fMRI, and PET);
• AI-assisted brain–computer interfaces;
• Signal processing techniques (EEG, MEG, EMG, and NIRS) using intelligent models;
• Generative models for neural data simulation;
• Algorithms for natural language processing;
• Optimization algorithms for neuroprosthetics and implantable devices;
• Personalized neurorehabilitation using AI;
• Detection and prediction of cognitive disorders via AI;
• Three-dimensional bioprinting of neural tissues and scaffolds guided by AI;
• Clinical decision support systems in neurology;
• Analysis of large-scale neurological datasets (neurological big data);
• Neuroeducation assisted by intelligent simulators.

Some examples of applications include the following:

• Early diagnosis of neurodegenerative diseases;
• Intelligent monitoring of patients with brain injuries;
• Adaptive deep brain stimulation;
• Modeling of biological neural networks;
• Neurosurgical planning with augmented reality;
• AI-based neurofeedback systems;
• Personalized therapies for autism spectrum disorders or ADHD;

EMG signal processing for classification or prediction of neuromuscular diseases.

Prof. Dr. Juvenal Rodriguez-Resendiz
Dr. Gerardo I. Pérez-Soto
Dr. Karla Anhel Camarillo-Gómez
Dr. Saul Tovar-Arriaga
Dr. Marcos Aviles
Guest Editors

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• neuroengineering
• artificial intelligence
• machine learning
• neuroimaging
• EEG signal processing
• neurodegenerative diseases
• AI-assisted diagnosis
• neurorehabilitation
• brain–computer interfaces
• generative models
• neuroprosthetics
• deep brain stimulation
• clinical decision systems
• natural language processing in neurology