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Technologies
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Advances in Biomedical Engineering and Artificial Intelligence for Neurological Health
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Advances in Biomedical Engineering and Artificial Intelligence for Neurological Health

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3027

Special Issue Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

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Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Queretaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Gerardo I. Pérez-Soto

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Guest Editor
Engineering Faculty, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; mechanical design; applied mechanics; theoretical kinematics
Special Issues, Collections and Topics in MDPI journals
Dr. Karla Anhel Camarillo-Gómez

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Guest Editor
Department of Mechanical Engineering, Tecnológico Nacional de México en Celaya, Celaya 38010, México
Interests: robotics; biomechanics; control systems
Special Issues, Collections and Topics in MDPI journals
Dr. Saul Tovar-Arriaga

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Guest Editor
Faculty of Engineering, Autonomous University of Queretaro, Cerro de las Campanas S/N, Santiago de Queretaro 76010, Queretaro, Mexico
Interests: image-based diagnosis; artificial intelligence; medical robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Marcos Aviles

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Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Queretaro 76010, Mexico
Interests: EMG; EEG; machine learning; metaheuristics; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 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

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Further information on MDPI's Special Issue policies can be found here.

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Published Papers (2 papers)

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20 pages, 3686 KB  
Article
Decoding Temporally Encoded 3D Objects from Low-Cost Wearable Electroencephalography
by John LaRocco, Qudsia Tahmina, Saideh Zia, Shahil Merchant, Jason Forrester, Eason He and Ye Lin
Technologies 2025, 13(11), 501; https://doi.org/10.3390/technologies13110501 - 1 Nov 2025
Viewed by 1288
Abstract
Decoding visual content from neural activity remains a central challenge at the intersections of engineering, neuroscience, and computational modeling. Prior work has primarily leveraged electroencephalography (EEG) with generative models to recover static images. In this study, we advance EEG-based decoding by introducing a [...] Read more.
Decoding visual content from neural activity remains a central challenge at the intersections of engineering, neuroscience, and computational modeling. Prior work has primarily leveraged electroencephalography (EEG) with generative models to recover static images. In this study, we advance EEG-based decoding by introducing a temporal encoding framework that approximates dynamic object transformations across time. EEG recordings from healthy participants (n = 20) were used to model neural representations of objects presented in “initial” and “later” states. Individualized classifiers trained on time-specific EEG signatures achieved high discriminability, with Random Forest models reaching a mean accuracy and standard deviation of 92 ± 2% and a mean AUC-ROC and standard deviation of 0.87 ± 0.10, driven largely by gamma- and beta-band activity at the frontal electrodes. These results confirm and extend evidence of strong interindividual variability, showing that subject-specific models outperform intersubject approaches in decoding temporally varying object representations. Beyond classification, we demonstrate that pairwise temporal encodings can be integrated into a generative pipeline to produce approximated reconstructions of short video sequences and 3D object renderings. Our findings establish that temporal EEG features, captured using low-cost open-source hardware, are sufficient to support the decoding of visual content across discrete time points, providing a versatile platform for potential applications in neural decoding, immersive media, and human–computer interaction. Full article
(This article belongs to the Special Issue Advances in Biomedical Engineering and Artificial Intelligence for Neurological Health)
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15 pages, 1111 KB  
Article
A Novel Methodology for Data Augmentation in Cognitive Impairment Subjects Using Semantic and Pragmatic Features Through Large Language Models
by Luis Roberto García-Noguez, Sebastián Salazar-Colores, Siddhartha Mondragón-Rodríguez and Saúl Tovar-Arriaga
Technologies 2025, 13(8), 344; https://doi.org/10.3390/technologies13080344 - 7 Aug 2025
Viewed by 1018
Abstract
In recent years, researchers have become increasingly interested in identifying traits of cognitive impairment using audio from neuropsychological tests. Unfortunately, there is no universally accepted terminology system that can be used to describe language impairment, and considerable variability exists between clinicians, making detection [...] Read more.
In recent years, researchers have become increasingly interested in identifying traits of cognitive impairment using audio from neuropsychological tests. Unfortunately, there is no universally accepted terminology system that can be used to describe language impairment, and considerable variability exists between clinicians, making detection particularly challenging. Furthermore, databases commonly used by the scientific community present sparse or unbalanced data, which hinders the optimal performance of machine learning models. Therefore, this study aims to test a new methodology for augmenting text data from neuropsychological tests in the Pitt Corpus database to increase classification and interpretability results. The proposed method involves augmenting text data with symptoms commonly present in subjects with cognitive impairment. This innovative approach has enabled us to differentiate between two groups in the database better than widely used text augmentation techniques. The proposed method yielded an increase in the metrics, achieving 0.8742 accuracy, 0.8744 F1-score, 0.8736 precision, and 0.8781 recall. It is shown that implementing large language models with commonly observed symptoms in the language of patients with cognitive impairment in text augmentation can improve the results in low-resource scenarios. Full article
(This article belongs to the Special Issue Advances in Biomedical Engineering and Artificial Intelligence for Neurological Health)
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