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Brain Activity Monitoring and Measurement (2nd Edition)
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Brain Activity Monitoring and Measurement (2nd Edition)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 1924

Special Issue Editors

Prof. Dr. Annabel Chen

E-Mail Website
Guest Editor
1. Director of the Centre for Research & Development in Learning (CRADLE@NTU), National Institute of Education, Nanyang Technological University, Singapore
2. Director (NTU) of the Centre for Lifelong Learning and Individualised Cognition (CLIC) in Collaboration with Cambridge University (an NRF-CREATE Programme) LKCMedicine, National Institute of Education, Nanyang Technological University, Singapore
Interests: fMRI; cognitive neuroscience
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gianluca Esposito

E-Mail Website
Guest Editor
Department of Psychology and Cognitive Sciences, University of Trento, 38100 Trento, Italy
Interests: social neuroscience; clinical psychology
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Bizzego

E-Mail Website
Guest Editor
Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy
Interests: physiological signal processing; statistical neuroimaging; artificial intelligence; reproducibility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past few decades, the emergence of remarkable novel sensing technologies and methodological approaches has contributed to advancing research on the human brain.

On the one hand, new-generation sensors have facilitated the collection of central and peripheral nervous system signals, allowing studies within new contexts and experimental settings (e.g., hyperscanning, real life).

On the other hand, novel analysis techniques (e.g., data fusion, artificial intelligence) have enabled a more efficient and robust extraction of brain activity indicators.

This Special Issue aims to report high-quality theoretical, analytical, and experimental investigations, including proof-of-concept, modeling, and practical-oriented studies, of new hardware and software applications aimed at monitoring and measuring brain activity.

This Special Issue welcomes high-quality papers containing original research results and survey articles in (but not limited to) the following fields:

  • Functional magnetic resonance imaging (fMRI);
  • Functional near-infrared spectroscopy (fNIRS);
  • Magnetic resonance spectroscopy (MRS);
  • Encephalography (EEG);
  • Real-life brain monitoring;
  • Signal processing and validation.

Prof. Dr. Annabel Chen
Prof. Dr. Gianluca Esposito
Dr. Andrea Bizzego
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. Sensors 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 2600 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

  • functional magnetic resonance imaging (fMRI)
  • functional near infrared spectroscopy (fNIRS)
  • magnetic resonance spectroscopy (MRS)
  • encephalography (EEG)
  • real-life brain monitoring
  • signal processing and validation

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Related Special Issue

Published Papers (3 papers)

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Research

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21 pages, 7669 KiB  
Article
Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest
by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu and Guangyu Bin
Sensors 2025, 25(7), 2332; https://doi.org/10.3390/s25072332 - 7 Apr 2025
Viewed by 373
Abstract
Objective: Clinically, patients in a coma after cardiac arrest are given the prognosis of “neurological recovery” to minimize discrepancies in opinions and reduce judgment errors. This study aimed to analyze the background patterns of electroencephalogram (EEG) signals from such patients to identify the [...] Read more.
Objective: Clinically, patients in a coma after cardiac arrest are given the prognosis of “neurological recovery” to minimize discrepancies in opinions and reduce judgment errors. This study aimed to analyze the background patterns of electroencephalogram (EEG) signals from such patients to identify the key indicators for assessing the prognosis after coma. Approach: Standard machine learning models were applied sequentially as feature selectors and filters. CatBoost demonstrated superior performance as a classification method compared to other approaches. In addition, Shapley additive explanation (SHAP) values were utilized to rank and analyze the importance of the features. Results: Our results indicated that the three different EEG features helped achieve a fivefold cross-validation receiver-operating characteristic (ROC) of 0.87. Our evaluation revealed that functional connectivity features contribute the most to classification at 70%. Among these, low-frequency long-distance functional connectivity (45%) was associated with a poor prognosis, whereas high-frequency short-distance functional connectivity (25%) was linked with a good prognosis. Burst suppression ratio is 20%, concentrated in the left frontal–temporal and right occipital–temporal regions at high thresholds (10/15 mV), demonstrating its strong discriminative power. Significance: Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. These findings provide a clinically actionable framework for advancing neurological prognosis and optimizing patient care. Full article
(This article belongs to the Special Issue Brain Activity Monitoring and Measurement (2nd Edition))
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20 pages, 2501 KiB  
Article
A Functional Magnetic Resonance Imaging Investigation of Hot and Cool Executive Functions in Reward and Competition
by Hsin-Yu Lin, Hoki Fung, Yifan Wang, Roger Chun-Man Ho and Shen-Hsing Annabel Chen
Sensors 2025, 25(3), 806; https://doi.org/10.3390/s25030806 - 29 Jan 2025
Viewed by 943
Abstract
Social and environmental influences are important for learning. However, the influence of reward and competition during social learning is less understood. The literature suggests that the ventromedial prefrontal cortex is implicated in hot executive functioning (EF), while the dorsolateral prefrontal cortex is related [...] Read more.
Social and environmental influences are important for learning. However, the influence of reward and competition during social learning is less understood. The literature suggests that the ventromedial prefrontal cortex is implicated in hot executive functioning (EF), while the dorsolateral prefrontal cortex is related to cool EF. In addition, reward processing deficits are associated with atypical connectivity between the nucleus accumbens and the dorsofrontal regions. Here, we used functional magnetic resonance imaging (fMRI) to determine the role of hot and cool EF in reward processing and their relationship to performance under social competition. We adapted a reward-based n-back task to examine the neural correlates of hot and cool EF and the reward influence on performance during competition. A total of 29 healthy adults showed cortical activation associated with individual differences in EF abilities during fMRI scans. Hot and cool EF activated distinct networks in the right insula, hippocampus, left caudate nucleus, and superior parietal gyrus during the no-competition task, while they differentially activated the right precuneus and caudate nucleus in the competition condition. Further analysis revealed correlations between the Hot–Cool network and reward sensitivity and risk-taking behaviour. The findings provided further insights into the neural basis of hot and cool EF engagement in the socio-emotional regulation for learning. Full article
(This article belongs to the Special Issue Brain Activity Monitoring and Measurement (2nd Edition))
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Review

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20 pages, 386 KiB  
Review
Effects of Mobile Electromagnetic Exposure on Brain Oscillations and Cortical Excitability: Scoping Review
by Azadeh Torkan, Maryam Zoghi, Negin Foroughimehr, Ali Yavari and Shapour Jaberzadeh
Sensors 2025, 25(9), 2749; https://doi.org/10.3390/s25092749 - 26 Apr 2025
Viewed by 158
Abstract
With the widespread adoption of smartphones, concerns about increased exposure to non-ionizing radiofrequency have emerged. This scoping review examines the effects of mobile phone exposure on neural oscillations and cortical excitability, focusing on both motor and non-motor regions of the cerebral cortex. A [...] Read more.
With the widespread adoption of smartphones, concerns about increased exposure to non-ionizing radiofrequency have emerged. This scoping review examines the effects of mobile phone exposure on neural oscillations and cortical excitability, focusing on both motor and non-motor regions of the cerebral cortex. A scoping review identified seventy-eight studies that involved healthy individuals and employed electroencephalography and only two studies that investigated transcranial magnetic stimulation as primary technical tools. The findings suggest that mobile phone exposure may affect brain oscillations and cortical excitability. However, inconsistencies in experimental methods across studies make it difficult to draw definitive conclusions. Additionally, research on fifth-generation technology, particularly mmWave exposure from next-generation mobile networks, remains limited and needs further exploration. These gaps highlight the need for more in-depth studies on how mobile phone exposure impacts brain function. Full article
(This article belongs to the Special Issue Brain Activity Monitoring and Measurement (2nd Edition))
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