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EEG Signal Processing Techniques and Applications—3rd Edition

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 24255

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Prof. Dr. Yifan Zhao

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Centre for Life-Cycle Engineering and Management, Cranfield University, Cranfield MK43 0BT, UK
Interests: deep learning; computer vision; digital twinning; sensor technologies
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Dr. Fei He

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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
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Dr. Yuzhu Guo

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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
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Dr. Hua-Liang Wei

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School of Electrical and Electronic Engineering, University of Sheffield, Sheffield S1 3JD, UK
Interests: nonlinear system identification; signal processing; explainable artificial intelligence and interpretable machine learning; intelligent diagnosis; complex systems and signals in climate, space, and healthcare
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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

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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

EEG Signal Processing Techniques and Applications—2nd Edition in Sensors (19 articles)

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Research

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15 pages, 1621 KB

Open AccessArticle

Role of Electroencephalography in the Assessment of Cortical Responses Elicited by Music Therapy in Burn Patients Undergoing Intensive Care

by Erica Iammarino, Alessia Baldoncini, Arianna Gagliardi, Laura Burattini and Ilaria Marcantoni

Sensors 2026, 26(8), 2358; https://doi.org/10.3390/s26082358 - 11 Apr 2026

Viewed by 299

Abstract

Music therapy (MT) is increasingly being integrated into intensive care unit (ICU) settings to modulate pain, stress, and emotional dysregulation. Although clinically promising, objective biomarkers for quantifying its neurophysiological effects are still missing. In this context, the electroencephalogram (EEG) represents a valid tool to assess cortical dynamics associated with cognitive–affective engagement elicited by MT. Our study aims to evaluate the role of electroencephalography as an objective tool for monitoring cortical responses to MT in the ICU. EEGs acquired from nine burn patients undergoing MT in the ICU were considered. Signals were preprocessed to improve the signal-to-noise ratio. Then, six frequency bands (delta, theta, alpha, beta, gamma, and sensorimotor rhythm) were extracted to compute band powers and derive 37 involvement indexes, which were statistically compared across three experimental phases: before, during, and after MT. Results demonstrate that involvement indexes effectively capture neurophysiological shifts induced by MT. Significant differences were observed in 22 indexes when comparing During-MT and Post-MT phases, with 2 indexes being statistically different also when comparing During-MT and Pre-MT phases; 5 indexes differed statistically when comparing Pre-MT and Post-MT phases. These results suggest a transient cortical engagement elicited during MT in ICU settings. Our findings align with previous research reporting EEG (and certain EEG-derived involvement indexes) sensitivity to capture music-induced cognitive and emotional modulation. This confirms electroencephalography potential to objectively reflect MT effects and support its integration in multidisciplinary burn care; however, analysis on larger cohorts is necessary to validate EEG as a clinical tool in MT. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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19 pages, 11709 KB

Open AccessArticle

Dual-Manifold Contrastive Learning for Robust and Real-Time EEG Motor Decoding

by Chengsi Hu, Qing Liu, Chenying Xu, Guanglin Li and Yongcheng Li

Sensors 2026, 26(6), 1783; https://doi.org/10.3390/s26061783 - 12 Mar 2026

Viewed by 493

Abstract

Brain–computer interfaces (BCIs) have great potential for consumer electronics, as they enable the decoding of brain activity to control external devices and assist human–computer interaction. However, current decoding methods for BCIs face several challenges, such as low accuracy, poor stability under electrode shift, and slow processing for real-time use. In this paper, we propose a hybrid decoding framework designed to address the challenges of current EEG decoding methods. Our method combines manifold learning with contrastive learning. The core of our method lies in a dual-manifold model that uses non-negative matrix factorization (NMF) and a contrastive manifold learning framework to extract clear and useful features from brain signals. To improve decoding stability, we introduce a joint training strategy that enhances feature learning. Furthermore, the system is optimized for real-time interaction, reducing the system latency to 100 ms. We collect EEG signals from 15 subjects performing motor execution tasks and 10 subjects performing motor imagery tasks to construct a motor EEG dataset. On this dataset, the proposed method achieves superior decoding performance, reaching F1-scores of 0.7382 for the motor imagery tasks and 0.8361 for the motor execution tasks. Furthermore, the method maintains robustness even with reduced electrode counts and altered spatial distributions, highlighting its potential as a decoding solution for reliable and portable BCI systems. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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20 pages, 4383 KB

Open AccessArticle

Assessment of the Psycho-Emotional State Induced by Open-Skill Sport Activity: An Electroencephalography-Based Study

by Erica Iammarino, Ilaria Marcantoni, Sebastiano Grillo and Laura Burattini

Sensors 2026, 26(4), 1198; https://doi.org/10.3390/s26041198 - 12 Feb 2026

Cited by 1 | Viewed by 479

Abstract

Electroencephalography (EEG) is an effective tool for monitoring the psycho-emotional state induced by open-skill sport activities characterized by dynamic environments and unpredictable situations, offering objective insights into mental engagement. This study aims to characterize the psycho-emotional state induced by table tennis sport activity by exploiting EEG-derived biomarkers. The ‘Real World Table Tennis’ database was analyzed, which includes EEG signals of 25 subjects acquired before, during and after playing table tennis. After preprocessing, 30-s EEG epochs were recursively extracted every 5 s. For each epoch, EEG rhythms were extracted and combined to obtain 37 engagement indexes, defined as ratios of two or more EEG rhythm powers. Median trends of each index were obtained for five cortical regions, and the Wilcoxon signed-rank test was applied to assess significant temporal changes. Results show that engagement indexes can effectively characterize psycho-emotional dynamics during table tennis, capturing the transition from resting to game phase in all cortical regions and exhibiting an increasing trend when having beta/alpha in their definition, and a decreasing trend when having high-frequency rhythms in the denominator. Our findings demonstrate the feasibility of using engagement indexes to monitor psycho-emotional states induced by open-skill sports and provide a framework for investigating mental engagement over time. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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26 pages, 2792 KB

Open AccessArticle

Analysis of Short-Term Subjective Well-Being/Comfort and Its Correlation to Different EEG Metrics

by Betty Wutzl, Kenji Leibnitz, Yuichi Ohsita and Masayuki Murata

Sensors 2026, 26(2), 446; https://doi.org/10.3390/s26020446 - 9 Jan 2026

Viewed by 444

Abstract

Finding a correlation between physiological measures and subjective well-being (SWB) or comfort has been an active research area in recent years. We focus on short-term SWB measures and their correlation to electroencephalography (EEG) signals in an office environment. We recorded EEG from 30 participants and asked them to report their SWB every 30 s. We analyzed the correlation between the relative power of different frequency bands at various sensor locations and SWB via k-nearest neighbor (k-NN) classification and linear regression. We also analyzed the correlation of the time series themselves at different sensor locations and how they can be classified into different SWB values via k-NN. Then, we tried to cluster participants into subgroups that had a similar correlation between their EEG recordings and their reported SWB. We found that a correlation between relative power and SWB also holds for short terms. However, the results of every single participant of all analyses vary substantially, and we could not find any consistent clustering into subgroups. That implies a huge individuality when it comes to EEG measures and reported short-term SWB. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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20 pages, 2616 KB

Open AccessArticle

MS-TSEFNet: Multi-Scale Spatiotemporal Efficient Feature Fusion Network

by Weijie Wu, Lifei Liu, Weijie Chen, Yixin Chen, Xingyu Wang, Andrzej Cichocki, Yunhe Lu and Jing Jin

Sensors 2026, 26(2), 437; https://doi.org/10.3390/s26020437 - 9 Jan 2026

Viewed by 491

Abstract

Motor imagery signal decoding is an important research direction in the field of brain–computer interfaces, which aim to judge the motor imagery state of an individual by analyzing electroencephalogram (EEG) signals. Deep learning technology has been gradually applied to EEG classification, which can automatically extract features. However, when processing complex EEG signals, the existing decoding models cannot effectively fuse features at different levels, resulting in limited classification performance. This study proposes a multi-scale spatiotemporal efficient feature fusion network (MS-TSEFNet), which learns the dynamic changes in EEG signals at different time scales through multi-scale convolution modules and combines the spatial attention mechanism to efficiently capture the spatial correlation between electrodes in EEG signals. In addition, the network adopts an efficient feature fusion strategy to deeply fuse features at different levels, thereby improving the expression ability of the model. In the task of motor imagery signal decoding, MS-TSEFNet shows higher accuracy and robustness. We use the public BCIC-IV2a, BCIC-IV2b and ECUST datasets for evaluation. The experimental results show that the average classification accuracy of MS-TSEFNet reaches 80.31%, 86.69% and 71.14%, respectively, which is better than the current state-of-the-art algorithms. We conducted an ablation experiment to further verify the effectiveness of the model. The experimental results showed that each module played an important role in improving the final performance. In particular, the combination of the multi-scale convolution module and the feature fusion module significantly improved the model’s ability to extract the spatiotemporal features of EEG signals. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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24 pages, 4080 KB

Open AccessArticle

MCRBM–CNN: A Hybrid Deep Learning Framework for Robust SSVEP Classification

by Depeng Gao, Yuhang Zhao, Jieru Zhou, Haifei Zhang and Hongqi Li

Sensors 2025, 25(24), 7456; https://doi.org/10.3390/s25247456 - 8 Dec 2025

Viewed by 796

Abstract

The steady-state visual evoked potential (SSVEP), a non-invasive EEG modality, is a prominent approach for brain–computer interfaces (BCIs) due to its high signal-to-noise ratio and minimal user training. However, its practical utility is often hampered by susceptibility to noise, artifacts, and concurrent brain activities, complicating signal decoding. To address this, we propose a novel hybrid deep learning model that integrates a multi-channel restricted Boltzmann machine (RBM) with a convolutional neural network (CNN). The framework comprises two main modules: a feature extraction module and a classification module. The former employs a multi-channel RBM to unsupervisedly learn latent feature representations from multi-channel EEG data, effectively capturing inter-channel correlations to enhance feature discriminability. The latter leverages convolutional operations to further extract spatiotemporal features, constructing a deep discriminative model for the automatic recognition of SSVEP signals. Comprehensive evaluations on multiple public datasets demonstrate that our proposed method achieves competitive performance compared to various benchmarks, particularly exhibiting superior effectiveness and robustness in short-time window scenarios. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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28 pages, 6983 KB

Open AccessArticle

Enhancing Signal and Network Integrity: Evaluating BCG Artifact Removal Techniques in Simultaneous EEG-fMRI Data

by Perihan Gülşah Gülhan and Güzin Özmen

Sensors 2025, 25(22), 7036; https://doi.org/10.3390/s25227036 - 18 Nov 2025

Viewed by 1200

Abstract

Simultaneous Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) provide a powerful framework for investigating brain dynamics; however, ballistocardiogram (BCG) artifacts in EEG compromise signal quality and limit the assessment of brain connectivity. This study evaluated three widely used artifact removal methods—Average Artifact Subtraction (AAS), Optimal Basis Set (OBS), and Independent Component Analysis (ICA)—together with two hybrid approaches (AAS + ICA and OBS + ICA). Unlike previous studies that focused solely on signal-level metrics, we adopted a holistic framework that combined signal quality indicators with graph-theoretical analysis of EEG-fMRI connectivity in static and dynamic contexts. The results show that AAS provides the best signal quality, whereas OBS better preserves structural similarity. ICA, although weaker in terms of signal metrics, demonstrates sensitivity to frequency-specific patterns in dynamic graphs. Hybrid methods yield benefits, with OBS + ICA producing the lowest p-values across frequency band pairs (e.g., theta–beta and delta–gamma), particularly in dynamic graphs. Topological analyses revealed that artifact removal significantly affected network structure, with dynamic analyses showing more pronounced frequency-specific effects than static analyses. High-frequency bands, such as beta and gamma, exhibit stronger differentiation under dynamic conditions. Overall, this study offers new insights into the relationship between artifact removal and brain network integrity, emphasizing the need for multimodal and frequency-sensitive evaluation strategies. The findings guide preprocessing decisions in EEG-fMRI studies and clarify how methodological choices shape the interpretation of brain connectivity. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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17 pages, 1825 KB

Open AccessArticle

STCCA: Spatial–Temporal Coupled Cross-Attention Through Hierarchical Network for EEG-Based Speech Recognition

by Liang Dong, Hengyi Shao, Lin Zhang and Lei Li

Sensors 2025, 25(21), 6541; https://doi.org/10.3390/s25216541 - 23 Oct 2025

Viewed by 1097

Abstract

Speech recognition based on Electroencephalogram (EEG) has attracted considerable attention due to its potential in communication and rehabilitation. Existing methods typically process spatial and temporal features with sequential, parallel, or constrained feature fusion strategies. However, the intricate cross-relationships between spatial and temporal features remain underexplored. To address these limitations, we propose a spatial–temporal coupled cross-attention mechanism through a hierarchical network, named STCCA. The proposed STCCA consists of three key components: local feature extraction module (LFEM), coupled cross-attention (CCA) fusion module, and global feature extraction module (GFEM). The LFEM employs CNNs to extract local temporal and spatial features, while the CCA fusion module leverages a dual-directional attention mechanism to establish deep interactions between temporal and spatial features. The GFEM uses multi-head self-attention layers to model long-range dependencies and extract global features comprehensively. STCCA is validated on three EEG-based speech datasets, achieving accuracies of 45.45%, 25.91%, and 29.07%, corresponding to improvements of 1.95%, 3.98%, and 1.98% over the comparison models. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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15 pages, 604 KB

Open AccessArticle

Converging Minds: EEG Synchrony During Communication About Moral Decision-Making in Dyadic Interactions

by Roberta A. Allegretta, Katia Rovelli and Michela Balconi

Sensors 2025, 25(13), 4239; https://doi.org/10.3390/s25134239 - 7 Jul 2025

Cited by 3 | Viewed by 2184

Abstract

Communication about moral decision-making involves complex emotional and cognitive processes, especially in critical situations. This study adopted a hyperscanning paradigm to explore neural convergence during moral negotiation. Twenty-six healthy young adults (mean age = 23.59 years; 16 women, 10 men), with no neurological or psychiatric conditions, were paired into 13 same-gender dyads at the Università Cattolica del Sacro Cuore. Each dyad discussed a medical moral dilemma while their electrophysiological (EEG) activity was simultaneously recorded. Participants were first categorized according to their Dominant Reasoning Profile (DRP) (cognitive or affective), and subsequently convergence in DRP within the dyads was established. EEG band dissimilarities within each dyad were analyzed across frontal, temporo-central, and parieto-occipital regions. The results revealed significantly greater dissimilarity in frontal delta-band activity compared to parieto-occipital areas, regardless of the dyad’s DRP. Such results might suggest different emotional and motivational reactions between the two individuals, reflecting a broader gap in how the moral decision-making process was interpreted and internalized by each member, despite their DRP. The EEG hyperscanning paradigm proves useful in the study and understanding of the neural mechanisms involved in social interaction about morally sensitive decisions and provides novel insights into dyadic brain dynamics. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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17 pages, 3490 KB

Open AccessArticle

Four-Dimensional Adjustable Electroencephalography Cap for Solid–Gel Electrode

by Junyi Zhang, Deyu Zhao, Yue Li, Gege Ming and Weihua Pei

Sensors 2025, 25(13), 4037; https://doi.org/10.3390/s25134037 - 28 Jun 2025

Viewed by 2417

Abstract

Currently, the electroencephalogram (EEG) cap is limited to a finite number of sizes based on head circumference, lacking the mechanical flexibility to accommodate the full range of skull dimensions. This reliance on head circumference data alone often results in a poor fit between the EEG cap and the user’s head shape. To address these limitations, we have developed a four-dimensional (4D) adjustable EEG cap. This cap features an adjustable mechanism that covers the entire cranial area in four dimensions, allowing it to fit the head shapes of nearly all adults. The system is compatible with 64 channels or lower electrode counts. We conducted a study with numerous volunteers to compare the performance characteristics of the 4D caps with the commercial (COML) caps in terms of contact pressure, preparation time, wearing impedance, and performance in brain–computer interface (BCI) applications. The 4D cap demonstrated the ability to adapt to various head shapes more quickly, reduce impedance during testing, and enhance measurement accuracy, signal-to-noise ratio (SNR), and comfort. These improvements suggest its potential for broader application in both laboratory settings and daily life. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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19 pages, 2574 KB

Open AccessArticle

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

Cited by 7 | Viewed by 3485

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 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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 2602

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 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 KB

Open AccessArticle

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 2098

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 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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55 pages, 931 KB

Open AccessEditor’s ChoiceReview

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

Cited by 6 | Viewed by 4698

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 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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