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Sensors and Sensing Technologies for Neuroscience
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Sensors and Sensing Technologies for Neuroscience

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 3084

Special Issue Editor

Dr. Paolo Benna

E-Mail Website
Guest Editor
Department of Neurosciences, University of Turin, Turin, Italy
Interests: epilepsy; EEG; evoked potentials; history of neurology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The nervous system produces numerous signals. Many of these (especially those involving motor activity) are easy or relatively easy to receive or uptake. However, it is far more complex a task to capture signals suitable for understanding the mechanisms that underlie nervous function and the alterations to the mechanisms that constitute the basis of neurological diseases. Historically, the most robust means of studying nervous function has been the study of electrical and magnetic epiphenomena. These can be captured in a non-invasive way, allowing an understanding of states of vigilance and, in the pathological field, of epileptic phenomena.

The possibility of interfacing in a more in-depth way with the various components of the nervous system has greatly advanced in recent years thanks to the availability of new techniques and devices, and to the possibility of analyzing and elaborating in depth phenomena that are already known, but whose complexity escaped a necessarily rudimentary investigation. It has thus been possible to take steps, for now only preliminary, in order potentially began to establish an understanding of more complex phenomena, such as the neuronal interactions that are at the basis of consciousness and, on the pathological side, the modifications of the electrical activity preceding the occurrence of epileptic seizure (with obvious implications for better clinical management of the disorder).

A multidisciplinary approach (bioengineering, materials science, clinical disciplines, bioethics) will be necessary to realize the understanding of the physiology and pathology of these mechanisms that will allow—within the limits imposed by ethics—researchers to interface directly with the nervous system (brain-computer interface systems) and its bidirectional manipulation.

This Special Issue aims to publish contributions related to the study of ways in which it is possible to deepen the understanding, in a non-invasive way, of the signals emitted by the nervous system and their correlation with significant aspects of neural functioning and its pathological deviations. Sensors is an advanced forum for the advancement of science of sensors and sensing technologies. In accordance with the aims of the journal, reviews and research papers concerning the capture of biophysical and biochemical signals of neural origin will be considered for publication in this Special Issue.

Dr. Paolo Benna
Guest Editor

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

  • central nervous system
  • EEG
  • consciousness
  • brain–computer interface
  • epilepsy
  • interdisciplinary studies
  • sensors
  • sensing technologies

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Published Papers (2 papers)

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13 pages, 2146 KiB  
Article
Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning
by Zhuo Wang, Avia Noah, Valentina Graci, Emily A. Keshner, Madeline Griffith, Thomas Seacrist, John Burns III, Ohad Gal and Allon Guez
Sensors 2024, 24(23), 7779; https://doi.org/10.3390/s24237779 - 5 Dec 2024
Cited by 1 | Viewed by 1098
Abstract
Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging to detect in real time, as they often occur suddenly and with little warning, highlighting the need for innovative detection methods. [...] Read more.
Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging to detect in real time, as they often occur suddenly and with little warning, highlighting the need for innovative detection methods. This study aimed to assist in the advancement of an accurate and efficient fall detection system using electroencephalogram (EEG) data to recognize the reaction to a postural disturbance. We employed a state-space-based system identification approach to extract features from EEG signals indicative of reactions to postural perturbations and compared its performance with those of traditional autoregressive (AR) and Shannon entropy (SE) methods. Using EEG epochs starting from 80 ms after the onset of the event yielded improved performance compared with epochs that started from the onset. The classifier trained on the EEG data achieved promising results, with a sensitivity of up to 90.9%, a specificity of up to 97.3%, and an accuracy of up to 95.2%. Additionally, a real-time algorithm was developed to integrate the EEG and accelerometer data, which enabled accurate fall detection in under 400 ms and achieved an over 99% accuracy in detecting unexpected falls. This research highlights the potential of using EEG data in conjunction with other sensors for developing more accurate and efficient fall detection systems, which can improve the safety and quality of life for elderly adults and other vulnerable individuals. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Neuroscience)
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16 pages, 1294 KiB  
Article
Near-Infrared Spectroscopy for Neonatal Sleep Classification
by Naser Hakimi, Emad Arasteh, Maren Zahn, Jörn M. Horschig, Willy N. J. M. Colier, Jeroen Dudink and Thomas Alderliesten
Sensors 2024, 24(21), 7004; https://doi.org/10.3390/s24217004 - 31 Oct 2024
Cited by 1 | Viewed by 1318
Abstract
Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, [...] Read more.
Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model’s superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Neuroscience)
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