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Electronics
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Feature Papers in Bioelectronics: 2025–2026 Edition
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Feature Papers in Bioelectronics: 2025–2026 Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 3395

Special Issue Editors

Prof. Dr. Enzo Pasquale Scilingo

E-Mail Website
Guest Editor
Department of Information Engineering, School of Engineering, University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy
Interests: wearable monitoring systems; human–computer interfaces; biomedical and biomechanical signal processing; control and instrumentation
Special Issues, Collections and Topics in MDPI journals
Dr. Gianluca Rho

E-Mail Website
Guest Editor
Department of Information Engineering, School of Engineering, University of Pisa, Research Center “E. Piaggio”, Pisa, Italy
Interests: biomedical signal processing; neuroscience; EEG; brain connectivity; dynamic causal modelling; electrodermal activity; physiological modelling; wearable devices; mobile health technologies

Special Issue Information

Dear Colleagues,

We are pleased to announce the launch of this new Special Issue titled “Feature Papers in Bioelectronics: 2025–2026 Edition.” Bioelectronics is an expanding, multidisciplinary field that bridges electronics and biomedical engineering, with a broad range of applications. Research on bioelectronic devices and systems is rapidly evolving, offering innovative technologies to support both healthy individuals and patients in managing and optimizing their health, well-being, and daily activities. This Special Issue aims to showcase high-quality contributions that reflect the most significant and cutting-edge developments in all areas of bioelectronics research.

In this Special Issue, original research articles and reviews are welcome. Research areas may include—but are not limited to—the following topics:

  • Biosignal acquisition;
  • Wearable and unobtrusive applications for health monitoring;
  • Sensor fusion and multimodal healthcare sensors;
  • AI-driven decision support systems for disease management;
  • Data-driven and model-based biosignal processing;
  • Hardware applications for vital sign detection;
  • Neuromodulation and stimulation;
  • Bioinspired electronics;
  • Miniaturized electronics and implantable sensors;
  • Wireless biosensors and sensor technology;
  • Mobile and lab-on-a-chip healthcare microsystems;
  • Neurorehabilitation engineering;
  • Biorobotics applications;
  • Human-computer interfaces;
  • Biofeedback applications;
  • Bioinformatics for healthcare engineering;
  • Bioimaging;
  • Computer vision for healthcare applications;
  • Medical data mining;
  • Biometric systems;
  • Digital twin technologies for personalized health simulations;
  • Ethical aspects of bioelectronics.

We look forward to receiving your contributions.

Prof. Dr. Enzo Pasquale Scilingo
Dr. Gianluca Rho
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • biosignal acquisition
  • biosignal processing
  • wearable sensors
  • artificial intelligence
  • machine learning
  • bioelectronics
  • biosensors
  • rehabilitation systems
  • ethics

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

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Research

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23 pages, 2145 KB  
Article
Seeing Through Touch: A Stereo-Vision Vibrotactile Aid for Visually Impaired People
by Claudia Presicci, Giulia Ballardini, Giorgia Marchesi, Paolo Robutti, Matteo Moro, Camilla Pierella, Andrea Canessa and Maura Casadio
Electronics 2026, 15(7), 1511; https://doi.org/10.3390/electronics15071511 - 3 Apr 2026
Viewed by 292
Abstract
Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external [...] Read more.
Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external processing, or provide unintuitive feedback. This work presents a wearable stereo-vision-based vibrotactile system for real-time obstacle detection and navigation assistance. The device combines an off-the-shelf stereo camera integrated with a simultaneous localization and mapping framework to perceive spatial geometry and detect obstacles in the user’s path. Two stereo-matching methods were implemented to estimate depth: a block-based algorithm optimized for low-latency performance and a semi-global approach providing denser depth maps. Detected obstacles are translated into distinct vibration patterns delivered through four skin-contact body-mounted actuators encoding both direction and distance. The system was evaluated with blindfolded sighted, visually impaired, and blind participants. Both stereo approaches supported reliable real-time guidance and high obstacle-avoidance rates, demonstrating robust performance on affordable, wearable hardware. These findings confirm the feasibility of real-time tactile guidance using commercially available components, marking a concrete step toward accessible navigation support that enhances safety and autonomy for blind and visually impaired individuals. Full article
(This article belongs to the Special Issue Feature Papers in Bioelectronics: 2025–2026 Edition)
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27 pages, 6678 KB  
Article
Unmasking Biases and Reliability Concerns in Convolutional Neural Networks Analysis of Cancer Pathology Images
by Michael Okonoda, Eder Martinez, Abhilekha Dalal and Lior Shamir
Electronics 2026, 15(6), 1182; https://doi.org/10.3390/electronics15061182 - 12 Mar 2026
Viewed by 352
Abstract
Convolutional Neural Networks have shown promising effectiveness in identifying different types of cancer from radiographs. However, the opaque nature of CNNs makes it difficult to fully understand the way they operate, limiting their assessment to empirical evaluation. Here we study the soundness of [...] Read more.
Convolutional Neural Networks have shown promising effectiveness in identifying different types of cancer from radiographs. However, the opaque nature of CNNs makes it difficult to fully understand the way they operate, limiting their assessment to empirical evaluation. Here we study the soundness of the standard practices by which CNNs are evaluated for the purpose of cancer pathology. Thirteen highly used cancer benchmark datasets were analyzed, using four common CNN architectures and different types of cancer, such as melanoma, carcinoma, colorectal cancer, and lung cancer. We compared the accuracy of each model with that of datasets made of cropped segments from the background of the original images that do not contain clinically relevant content. Because the rendered datasets contain no clinical information, the null hypothesis is that the CNNs should provide mere chance-based accuracy when classifying these datasets. The results show that the CNN models provided high accuracy when using the cropped segments, sometimes as high as 93%, even though they lacked biomedical information. These results show that some CNN architectures are more sensitive to bias than others. The analysis shows that the common practices of machine learning evaluation might lead to unreliable results when applied to cancer pathology. These biases are very difficult to identify, and might mislead researchers as they use available benchmark datasets to test the efficacy of CNN methods. Full article
(This article belongs to the Special Issue Feature Papers in Bioelectronics: 2025–2026 Edition)
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18 pages, 7561 KB  
Article
Large-Scale Real-World Smartphone Photoplethysmography Datasets for Vascular Assessment
by Stevan Jokić, Ivan Jokić, Nenad Gligorić, Aneta Kartali and Octavian M. Machidon
Electronics 2026, 15(5), 988; https://doi.org/10.3390/electronics15050988 - 27 Feb 2026
Viewed by 497
Abstract
The development of reliable smartphone-based methods for vascular assessment is limited by the scarcity of large-scale, high-quality, real-world photoplethysmography (PPG) datasets. This work introduces two openly reusable smartphone camera-based PPG datasets curated from over one million unconstrained recordings, designed to support vascular morphology [...] Read more.
The development of reliable smartphone-based methods for vascular assessment is limited by the scarcity of large-scale, high-quality, real-world photoplethysmography (PPG) datasets. This work introduces two openly reusable smartphone camera-based PPG datasets curated from over one million unconstrained recordings, designed to support vascular morphology analysis and vascular aging research. The first dataset comprises approximately 5000 high-fidelity PPG heartbeat templates labeled into four morphological classes based on dicrotic notch characteristics, enabling assessment of arterial waveform structure beyond chronological age. The second dataset contains about 10,000 demographically balanced PPG samples curated for chronological age regression using rigorous subject-level balancing and correlation-based quality control. A standardized processing pipeline is presented, including beat alignment, ensemble averaging, and objective signal acceptance criteria to ensure morphological stability. To validate dataset utility, multiple machine learning models were benchmarked using raw signals, second derivatives, and compact Gaussian representations, achieving classification accuracy up to 90.08% and age prediction error below 10 years. By prioritizing real-world data quality, transparency, and reuse, this work provides a robust foundation for scalable, interpretable, and reproducible research in smartphone-based vascular assessment. Full article
(This article belongs to the Special Issue Feature Papers in Bioelectronics: 2025–2026 Edition)
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Review

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23 pages, 392 KB  
Review
EEG Signal Processing Pipelines in the Study of Neurophysiological Characteristics of Gifted Primary School Children: A Scoping Review
by Eloy García-Pérez, Roberto Sánchez-Reolid, Alejandro L. Borja and Juan Carlos Pastor Vicedo
Electronics 2025, 14(23), 4607; https://doi.org/10.3390/electronics14234607 - 24 Nov 2025
Viewed by 1567
Abstract
This review systematically examines electroencephalography (EEG) studies on gifted children, focusing on the signal processing pipelines across acquisition, preprocessing, feature extraction, and analysis, and identifying opportunities for methodological standardisation relevant to educational research. Following PRISMA 2020 guidelines, a comprehensive search was carried out [...] Read more.
This review systematically examines electroencephalography (EEG) studies on gifted children, focusing on the signal processing pipelines across acquisition, preprocessing, feature extraction, and analysis, and identifying opportunities for methodological standardisation relevant to educational research. Following PRISMA 2020 guidelines, a comprehensive search was carried out in PubMed, Scopus, Web of Science, IEEE Xplore, and PsycINFO. From 197 records, 14 studies met the inclusion criteria and were analysed for EEG setup, preprocessing strategies, and analytical approaches, including event-related potentials, spectral and connectivity measures, and applications of machine learning. Substantial heterogeneity was observed in device configurations, preprocessing practices, and analytical choices, limiting cross-study comparability and the transfer of findings to educational contexts. Nevertheless, recurring neurophysiological markers were identified, such as P300, frontoparietal γ synchronisation, and θ–α modulations during cognitive tasks. Only a minority of studies implemented supervised classification methods, suggesting an underexplored potential for advanced data-driven approaches in paediatric EEG. Transparent and standardised EEG pipelines, with explicit reporting of filters, artefact thresholds, and rejection rates, are essential to enhance reproducibility and translational value. By framing EEG signal processing within an educational perspective, this review provides methodological guidance to support early identification, inform classroom practice, and strengthen the bridge between neuroscience and education. Full article
(This article belongs to the Special Issue Feature Papers in Bioelectronics: 2025–2026 Edition)
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