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EEG Signal Processing Techniques and Applications—3rd Edition
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EEG Signal Processing Techniques and Applications—3rd Edition

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 3455

Special Issue Editors

Prof. Dr. Yifan Zhao

E-Mail Website
Guest Editor
Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield MK43 0AL, UK
Interests: machine learning; artificial intelligence; human factors; pattern recognition; digital twins; instrumentation, sensors and measurement science; systems engineering; through-life engineering services
Special Issues, Collections and Topics in MDPI journals
Dr. Fei He

E-Mail Website
Guest Editor
Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2JH, UK
Interests: nonlinear signal processing; system identification; statistical machine learning; frequency-domain analysis; causality analysis; computational neuroscience
Special Issues, Collections and Topics in MDPI journals
Dr. Yuzhu Guo

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: brain dynamics and brain activities; brain–computer interfaces; AI for clinical disease diagnosis; neurorehabilitation; hybrid-augmented intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Hua-Liang Wei

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, The University of Sheffield, Sheffield S10 2TN, UK
Interests: bioscience signal processing; data modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is a well-established, non-invasive tool to record brain electrophysiological activity. It is economical, portable, easy to administer, and widely available in most hospitals. Compared with other neuroimaging techniques that provide information about the anatomical structure (e.g., MRI, CT, and fMRI), EEG offers ultra-high time resolution, which is critical in understanding brain function. The empirical interpretation of EEG is largely based on recognizing abnormal frequencies in specific biological states, the spatial-temporal and morphological characteristics of paroxysmal or persistent discharges, reactivity to external stimuli, and activation procedures or intermittent photic stimulation. Despite being useful in many instances, these practical approaches to interpreting EEGs can leave important dynamic and nonlinear interactions between various brain network anatomical constituents undetected within its recordings; as such, interactions are far beyond the observational capabilities of any specially trained physician in this field.

This Special Issue will provide a forum for original high-quality research on EEG signal pre-processing, modeling, analysis, and its applications in the time, space, frequency, or time–frequency domains. The applications of artificial intelligence and machine learning approaches in this topic are particularly welcomed. The types of applications to consider include but are not limited to the following:

  • Clinical studies.
  • Human factors.
  • Brain–machine interfaces.
  • Psychology and neuroscience.
  • Social interactions.

Prof. Dr. Yifan Zhao
Dr. Fei He
Dr. Yuzhu Guo
Dr. Hua-Liang Wei
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

  • electroencephalography
  • EEG signal processing
  • artificial intelligence in EEG data analysis
  • brain connectivity
  • time–frequency analysis
  • deep learning in EEG data analysis
  • machine learning techniques in EEG data analysis
  • computer-aided diagnosis systems

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Related Special Issue

Published Papers (4 papers)

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Research

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19 pages, 2574 KiB  
Article
EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks
by Bahman Abdi-Sargezeh, Sepehr Shirani, Antonio Valentin, Gonzalo Alarcon and Saeid Sanei
Sensors 2025, 25(2), 494; https://doi.org/10.3390/s25020494 - 16 Jan 2025
Viewed by 1138
Abstract
A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor [...] Read more.
A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor signals to intracranial EEG (iEEG) sensor signals recorded by foramen ovale sensors inserted into the brain. The model is based on a GAN structure in which a conditional GAN (cGAN) is combined with a variational autoencoder (VAE), named as VAE-cGAN. scEEG sensors are plagued by noise and suffer from low resolution. On the other hand, iEEG sensor recordings enjoy high resolution. Here, we consider the task of mapping the scEEG sensor information to iEEG sensors to enhance the scEEG resolution. In this study, our EEG data contain epileptic interictal epileptiform discharges (IEDs). The identification of IEDs is crucial in clinical practice. Here, the proposed VAE-cGAN is firstly employed to map the scEEG to iEEG. Then, the IEDs are detected from the resulting iEEG. Our model achieves a classification accuracy of 76%, an increase of, respectively, 11%, 8%, and 3% over the previously proposed least-square regression, asymmetric autoencoder, and asymmetric–symmetric autoencoder mapping models. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)
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14 pages, 3151 KiB  
Article
Neural Mass Modeling in the Cortical Motor Area and the Mechanism of Alpha Rhythm Changes
by Yuanyuan Zhang, Zhaoying Li, Hang Xu, Ziang Song, Ping Xie, Penghu Wei and Guoguang Zhao
Sensors 2025, 25(1), 56; https://doi.org/10.3390/s25010056 - 25 Dec 2024
Viewed by 922
Abstract
Investigating the physiological mechanisms in the motor cortex during rehabilitation exercises is crucial for assessing stroke patients’ progress. This study developed a single-channel Jansen neural mass model to explore the relationship between model parameters and motor cortex mechanisms. Firstly, EEG signals were recorded [...] Read more.
Investigating the physiological mechanisms in the motor cortex during rehabilitation exercises is crucial for assessing stroke patients’ progress. This study developed a single-channel Jansen neural mass model to explore the relationship between model parameters and motor cortex mechanisms. Firstly, EEG signals were recorded from 11 healthy participants under 20%, 40%, and 60% maximum voluntary contraction, and alpha rhythm power spectral density characteristics were extracted using the Welch power spectrum method. Furthermore, a single-channel neural mass model was constructed to analyze the impact of parameter variations on the average power of simulated signals. Finally, model parameters were adjusted to achieve feature fitting between the simulated signals and the average power of the alpha rhythm. Results showed that alpha rhythm average power in the contralateral cortical regions increased with higher grip force levels. Similarly, the power of the simulated signals also increased with specific parameter (J, Ge, and Gi) increases, closely approximating the measured EEG signal changes. The findings suggest that increasing grip force activates more motor neurons in the motor cortex and raises their firing rate. Neural mass modeling provides a computational neuroscience approach to understanding the dynamic changes in alpha rhythms in the motor cortex under different grip force levels. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)
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11 pages, 3042 KiB  
Article
A Case Study on EEG Signal Correlation Towards Potential Epileptic Foci Triangulation
by Theodor Doll, Thomas Stieglitz, Anna Sophie Heumann and Daniel K. Wójcik
Sensors 2024, 24(24), 8116; https://doi.org/10.3390/s24248116 - 19 Dec 2024
Viewed by 824
Abstract
The precise localization of epileptic foci with the help of EEG or iEEG signals is still a clinical challenge with current methodology, especially if the foci are not close to individual electrodes. On the research side, dipole reconstruction for focus localization is a [...] Read more.
The precise localization of epileptic foci with the help of EEG or iEEG signals is still a clinical challenge with current methodology, especially if the foci are not close to individual electrodes. On the research side, dipole reconstruction for focus localization is a topic of recent and current developments. Relatively low numbers of recording electrodes cause ill-posed and ill-conditioned problems in the inversion of lead-field matrices to calculate the focus location. Estimations instead of tissue conductivity measurements further deteriorate the precision of location tasks. In addition, time-resolved phase shifts are used to describe connectivity. We hypothesize that correlations over runtime approaches might be feasible to predict seizure foci with adequate precision. In a case study on EEG correlation in a healthy subject, we found repetitive periods of alternating high correlation in the short (20 ms) and long (300 ms) range. During these periods, a numerical determination of proportions of predominant latency and, newly established here, directionality is possible, which supports the identification of loops that, according to current opinion, manifest themselves in epileptic seizures. In the future, this latency and directionality analysis could support focus localization via dipole reconstruction using new triangulation calculations. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)
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Review

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55 pages, 913 KiB  
Review
Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods
by Zhishui You, Yuzhu Guo, Xiulei Zhang and Yifan Zhao
Sensors 2025, 25(10), 3178; https://doi.org/10.3390/s25103178 - 18 May 2025
Viewed by 59
Abstract
Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to [...] Read more.
Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)
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