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EEG Signal Processing for Sensing Technologies in Biomedical Engineering Applications: 3rd Edition

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 2808

Special Issue Editor

Dr. Yvonne Tran

E-Mail Website
Guest Editor
Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
Interests: biomedical signals; psychophysiology; injury; electroencephalography; heart rate variability; machine learning for rehabilitation medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research focused on brain electrical signals from electroencephalograms (EEGs) is gaining traction among the biomedical, psychology, engineering, and computer science fields. EEG signals have great potential for use in biomedical applications for the diagnosis, treatment, and monitoring of conditions that can alter brain activity, such as mental fatigue. The applications of EEG signals include the monitoring of brain diseases—such as epilepsy, brain tumors, and head and spinal injuries—and sleep disorders. Furthermore, controlling the environment with our mind has always been desirable; consequently, assistive technology applications using EEG signals such as brain–computer interfaces (BCI) as platforms for hands-free control have undergone substantial research. EEG measurement is reliable, relatively cheap, portable, and non-invasive, making it a key methodology for affordable and effective research, as well as a promising clinical and healthcare tool.

This Special Issue will contribute to the latest advancements pertaining to EEG signals for biomedical applications. We are inviting submissions of original research as well as review articles and new development reports focused on the topic at hand, “Using EEG Signals for Biomedical Applications”.

Topics of interest include (but are not limited to) the following:

  • Biomedical applications using EEG signals;
  • Assistive technologies using EEG;
  • Brain–computer interfaces;
  • EEG signal processing;
  • EEG for monitoring;
  • EEG as a biomarker;
  • The influence of conditions such as fatigue on brain activity;
  • EEG and sleep.

Dr. Yvonne Tran
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

  • EEG signals
  • biomedical applications
  • brain activity
  • brain–computer interface
  • EEG biomarkers
  • biomonitoring

Related Special Issue

Published Papers (4 papers)

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Research

15 pages, 2946 KiB  
Article
The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action
by Bo Yao, Chengzhen Wu, Xing Zhang, Junjie Yao, Jianchao Xue, Yu Zhao, Ting Li and Jiangbo Pu
Sensors 2024, 24(7), 2323; https://doi.org/10.3390/s24072323 - 05 Apr 2024
Viewed by 502
Abstract
This study explores the important role of assessing force levels in accurately controlling upper limb movements in human–computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of [...] Read more.
This study explores the important role of assessing force levels in accurately controlling upper limb movements in human–computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human–computer interface technologies. Full article
(This article belongs to the Special Issue EEG Signal Processing for Sensing Technologies in Biomedical Engineering Applications: 3rd Edition)
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26 pages, 3899 KiB  
Article
Implementation of a Real-Time Brain-to-Brain Synchrony Estimation Algorithm for Neuroeducation Applications
by Axel A. Mendoza-Armenta, Paula Blanco-Téllez, Adaliz G. García-Alcántar, Ivet Ceballos-González, María A. Hernández-Mustieles, Ricardo A. Ramírez-Mendoza, Jorge de J. Lozoya-Santos and Mauricio A. Ramírez-Moreno
Sensors 2024, 24(6), 1776; https://doi.org/10.3390/s24061776 - 09 Mar 2024
Viewed by 685
Abstract
This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of algorithm can provide useful information in the educational context, for instance, during teacher–student or student–student interactions. Positioned within the [...] Read more.
This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of algorithm can provide useful information in the educational context, for instance, during teacher–student or student–student interactions. Positioned within the context of neuroeducation and hyperscanning, this research addresses the need for biomarkers as metrics for feedback, a missing element in current teaching methods. Implementing the bispectrum technique with multiprocessing functions in Python, the algorithm effectively processes electroencephalography signals and estimates brain-to-brain synchronization between pairs of subjects during (competitive and collaborative) activities that imply specific cognitive processes. Noteworthy differences, such as higher bispectrum values in collaborative tasks compared to competitive ones, emerge with reliability, showing a total of 33.75% of significant results validated through a statistical test. While acknowledging progress, this study identifies areas of opportunity, including embedded operations, wider testing, and improved result visualization. Beyond academia, the algorithm’s utility extends to classrooms, industries, and any setting involving human interactions. Moreover, the presented algorithm is shared openly, to facilitate implementations by other researchers, and is easily adjustable to other electroencephalography devices. This research not only bridges a technological gap but also contributes insights into the importance of interactions in educational contexts. Full article
(This article belongs to the Special Issue EEG Signal Processing for Sensing Technologies in Biomedical Engineering Applications: 3rd Edition)
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20 pages, 1845 KiB  
Article
Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification
by Xin Xiong, Zhiran Sun, Aikun Wang, Jiancong Zhang, Jing Zhang, Chunwu Wang and Jianfeng He
Sensors 2024, 24(5), 1642; https://doi.org/10.3390/s24051642 - 02 Mar 2024
Viewed by 669
Abstract
The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive [...] Read more.
The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients. Full article
(This article belongs to the Special Issue EEG Signal Processing for Sensing Technologies in Biomedical Engineering Applications: 3rd Edition)
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14 pages, 3770 KiB  
Article
Two-Stage Atomic Decomposition of Multichannel EEG and the Previously Undetectable Sleep Spindles
by Piotr Durka, Marian Dovgialo, Anna Duszyk-Bogorodzka and Piotr Biegański
Sensors 2024, 24(3), 842; https://doi.org/10.3390/s24030842 - 28 Jan 2024
Viewed by 568
Abstract
We propose a two-step procedure for atomic decomposition of multichannel EEGs, based upon multivariate matching pursuit and dipolar inverse solution, from which atoms representing relevant EEG structures are selected according to prior knowledge. We detect sleep spindles in 147 polysomnographic recordings from the [...] Read more.
We propose a two-step procedure for atomic decomposition of multichannel EEGs, based upon multivariate matching pursuit and dipolar inverse solution, from which atoms representing relevant EEG structures are selected according to prior knowledge. We detect sleep spindles in 147 polysomnographic recordings from the Montreal Archive of Sleep Studies. Detection is compared with human scorers and two state-of-the-art algorithms, which find only about a third of the structures conforming to the definition of sleep spindles and detected by the proposed method. We provide arguments supporting the thesis that the previously undetectable sleep spindles share the same properties as those marked by human experts and previously applied methods, and were previously omitted only because of unfavorable local signal-to-noise ratios, obscuring their visibility to both human experts and algorithms replicating their markings. All detected EEG structures are automatically parametrized by their time and frequency centers, width duration, phase, and spatial location of an equivalent dipolar source within the brain. It allowed us, for the first time, to estimate the spatial gradient of sleep spindles frequencies, which not only confirmed quantitatively the well-known prevalence of higher frequencies in posterior regions, but also revealed a significant gradient in the sagittal plane. The software used in this study is freely available. Full article
(This article belongs to the Special Issue EEG Signal Processing for Sensing Technologies in Biomedical Engineering Applications: 3rd Edition)
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