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Applied Sciences
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EEG Recognition and Biomedical Signal Processing
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EEG Recognition and Biomedical Signal Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 265

Special Issue Editors

Dr. Andrea Giorgi

E-Mail Website
Guest Editor
Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
Interests: cognitive processes; human factors; decision making; signal processing
Dr. Christoph Hintermüller

E-Mail Website
Guest Editor
Institute for Biomedical Mechatronics, Johannes Kepler University, 4020 Linz, Austria
Interests: biosignal processing; cardiac electrophysiology; 3D imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is a widely used brain imaging technique that provides valuable insights into the brain's electrical activity. Recent technological advancements have significantly expanded its applications, particularly with the development of high-quality laboratory equipment and wearable devices. These innovations have made EEG more versatile, allowing recordings to be taken not only in traditional lab settings but also in real-world environments. As EEG technology becomes more accessible, understanding its signals and functional implications has grown in importance.

In addition to EEG, the ability to record other biosignals, such as heart activity, skin conductance, eye blinks, and respiration, has increased due to the rise of both medical and commercial devices. This growing acquisition of biosignals highlights the need for advanced processing and analysis techniques with which to extract meaningful information from the data.

This Special Issue, "EEG Recognition and Biomedical Signal Processing", seeks to address these needs by showcasing the latest advancements in signal processing techniques and their practical applications. Contributions are invited from a wide range of topics, from innovative methods of signal collection and processing to the use of these signals in recognizing psychophysical and physiological phenomena. Thus Special Issue aims to provide a comprehensive overview of the current progress and future directions in the field of biomedical signal processing.

Dr. Andrea Giorgi
Dr. Christoph Hintermüller
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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
  • signal processing
  • electroencephalography (EEG)
  • physiological monitoring
  • biomedical analysis

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Published Papers (1 paper)

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Research

24 pages, 9657 KiB  
Article
Electroencephalography-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency Interpretability
by Santiago Buitrago-Osorio, Julian Gil-González, Andrés Marino Álvarez-Meza, David Cardenas-Peña and Alvaro Orozco-Gutierrez
Appl. Sci. 2025, 15(9), 4804; https://doi.org/10.3390/app15094804 - 26 Apr 2025
Viewed by 148
Abstract
Chronic pain leads to not only physical discomfort but also psychological challenges, such as depression and anxiety, which contribute to a substantial healthcare burden. Pain detection and assessment remains a challenge due to its subjective nature. Current clinical methods may be inaccurate or [...] Read more.
Chronic pain leads to not only physical discomfort but also psychological challenges, such as depression and anxiety, which contribute to a substantial healthcare burden. Pain detection and assessment remains a challenge due to its subjective nature. Current clinical methods may be inaccurate or unfeasible for non-verbal patients. Consequently, Electroencephalography (EEG) has emerged as a promising non-invasive tool for pain detection. However, EEG-based pain detection faces challenges such as noise, volume conduction effects, and high inter-subject variability. Deep learning (DL) models have shown potential in overcoming these challenges by extracting nonlinear and discriminative patterns. Despite advancements, these models often require a subject-dependent approach and lack of interpretability. To address these limitations, we propose a threefold DL-based framework for coding EEG-based pain detection patterns. (i) We employ the Kernel Cross-Spectral Gaussian Functional Connectivity Network (KCS-FCnet) to code pairwise channel dependencies for pain detection. (ii) Furthermore, we introduce a frequency-based strategy for class activation mapping to visualize pertinent pain EEG features, thereby enhancing visual interpretability through spatio-frequency patterns. (iii) Further, to account for subject variability, we conduct cross-subject analysis and grouping, clustering individuals based on similar pain detection performance, functional connectivity patterns, sex, and age. We evaluate our model using the Brain Mediators of Pain dataset and demonstrate its robustness through subject-dependent and cross-subject generalization tasks for pain detection on non-verbal patients. Full article
(This article belongs to the Special Issue EEG Recognition and Biomedical Signal Processing)
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