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Latest Developments in Biopotential Sensor Development for Brain Signals
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Latest Developments in Biopotential Sensor Development for Brain Signals

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1293

Special Issue Editor

Prof. Dr. Walter Besio

E-Mail Website
Guest Editor
Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
Interests: electrodes; epilepsy; bi-directional brain computer interfacing

Special Issue Information

Dear Colleagues,

Biopotential sensors, specifically electrodes, play a crucial role in diagnosis, neuroscience, and medical research. These sensors are essential for accurately detecting and measuring electrical signals generated by biological tissues, particularly the brain. Over the past few years, the development of biopotential sensors, encompassing both invasive and noninvasive technologies, has seen significant advancements. This progress has opened new avenues for research and clinical applications.

This Special Issue is dedicated to exploring the latest advancements in the development and application of biopotential sensors for acquiring brain signals. It aims to highlight innovative sensors and methodologies that enhance the precision and reliability of brain signal acquisition. Additionally, this issue will welcome contributions that introduce novel techniques for analyzing brain biopotential signals, as well as practical applications of these technologies.

One of the exciting frontiers in this field is the integration of artificial intelligence with brain biopotential signals. This combination holds the potential to uncover new biomarkers for diagnosing brain diseases and disorders. Furthermore, it can significantly advance the field of brain–computer interfacing, enabling more effective communication and control systems for individuals with neurological conditions. By fostering interdisciplinary collaboration and innovation, this Special Issue aspires to push the boundaries of what is possible in neuroscience and medical research.

Prof. Dr. Walter Besio
Guest Editor

Manuscript Submission Information

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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

  • biopotential sensors
  • electrodes
  • brain signals
  • brain–computer interface

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Published Papers (2 papers)

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Research

14 pages, 2241 KB  
Article
Passive Brain–Computer Interface Using Textile-Based Electroencephalography
by Alec Anzalone, Emily Acampora, Careesa Liu and Sujoy Ghosh Hajra
Sensors 2025, 25(19), 6080; https://doi.org/10.3390/s25196080 - 2 Oct 2025
Abstract
Background: Passive brain–computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user’s cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on [...] Read more.
Background: Passive brain–computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user’s cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable. Full article
(This article belongs to the Special Issue Latest Developments in Biopotential Sensor Development for Brain Signals)
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18 pages, 2062 KB  
Article
Measuring Blink-Related Brainwaves Using Low-Density Electroencephalography with Textile Electrodes for Real-World Applications
by Emily Acampora, Sujoy Ghosh Hajra and Careesa Chang Liu
Sensors 2025, 25(14), 4486; https://doi.org/10.3390/s25144486 - 18 Jul 2025
Viewed by 576
Abstract
Background: Electroencephalography (EEG) systems based on textile electrodes are increasingly being developed to address the need for more wearable sensor systems for brain function monitoring. Blink-related oscillations (BROs) are a new measure of brain function that corresponds to brainwave responses occurring after [...] Read more.
Background: Electroencephalography (EEG) systems based on textile electrodes are increasingly being developed to address the need for more wearable sensor systems for brain function monitoring. Blink-related oscillations (BROs) are a new measure of brain function that corresponds to brainwave responses occurring after spontaneous blinking, and indexes neural processes as the brain evaluates new visual information appearing after eye re-opening. Prior studies have reported BRO utility as both a clinical and non-clinical biomarker of cognition, but no study has demonstrated BRO measurement using textile-based EEG devices that facilitate user comfort for real-world applications. Methods: We investigated BRO measurement using a four-channel EEG system with textile electrodes by extracting BRO responses using existing, publicly available EEG data (n = 9). We compared BRO effects derived from textile-based electrodes with those from standard dry Ag/Ag-Cl electrodes collected at the same locations (i.e., Fp1, Fp2, F7, F8) and using the same EEG amplifier. Results: Results showed that BRO effects measured using textile electrodes exhibited similar features in both time and frequency domains compared to dry Ag/Ag-Cl electrodes. Data from both technologies also showed similar performance in artifact removal and signal capture. Conclusions: These findings provide the first demonstration of successful BRO signal capture using four-channel EEG with textile electrodes, providing compelling evidence toward the development of a comfortable and user-friendly EEG technology that uses the simple activity of blinking for objective brain function assessment in a variety of settings. Full article
(This article belongs to the Special Issue Latest Developments in Biopotential Sensor Development for Brain Signals)
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