ReviewJournal of neuroengineering and rehabilitation2023
Generative adversarial networks in EEG analysis: an overview.
Review in Journal of neuroengineering and rehabilitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets.F1000Research · 2025Pooled it
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification.Brain informatics · 2026Article
- Neonatal Seizure Detection Based on Spatiotemporal Feature Decoupling and Domain-Adversarial Learning.Sensors (Basel, Switzerland) · 2026Article
- Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches.AIMS neuroscience · 2026Review
- Render EEG-Based Brain-Computer Interfaces Calibration-Free: Trade Space for Time in EEG Decoding.IEEE open journal of engineering in medicine and biology · 2026Article
- [Research on type 2 diabetes prediction algorithm based on photoplethysmography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform.Scientific reports · 2025Article
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.Sensors (Basel, Switzerland) · 2025Review
- Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.Frontiers in neuroinformatics · 2025Article
- Threats and Mitigation Strategies for Electroencephalography-Based Person Authentication.International journal of telemedicine and applications · 2025Review
- Evaluating pediatric peripheral neuromuscular disorders using deep neural networks on electrodiagnostic data.Clinical neurophysiology practice · 2025Article
- Augmenting Electroencephalogram Transformer for Steady-State Visually Evoked Potential-Based Brain-Computer Interfaces.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction.Journal of healthcare informatics research · 2024Article
- On the role of generative artificial intelligence in the development of brain-computer interfaces.BMC biomedical engineering · 2024Review
- Paradigm shift required for translational research on the brain.Experimental & molecular medicine · 2024Review
- A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.Brain sciences · 2024Article
- Applications for Deep Learning in Epilepsy Genetic Research.International journal of molecular sciences · 2023Review
- A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces.Scientific data · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.