Virtual electroencephalogram acquisition: a review on electroencephalogram generative methods

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dc.contributor.authorYou, Zhishui
dc.contributor.authorGuo, Yuzhu
dc.contributor.authorZhang, Xiulei
dc.contributor.authorZhao, Yifan
dc.date.accessioned2025-05-21T14:44:42Z
dc.date.available2025-05-21T14:44:42Z
dc.date.freetoread2025-05-21
dc.date.issued2025-05-02
dc.date.pubOnline2025-05-18
dc.description.abstractDriven 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.
dc.description.journalNameSensors
dc.description.sponsorshipThis work was supported in part by the National Key R&D Program of China (project number: 2023YFC2506600; task number: 2023YFC2506601), and by the National Natural Science Foundation of China (Grant No. 62273017).
dc.identifier.citationYou Z, Guo Y, Zhang X, Zhao Y. (2025) Virtual electroencephalogram acquisition: a review on electroencephalogram generative methods. Sensors, Volume 25, Issue 10, May 2025, Article number 3178
dc.identifier.eissn1424-8220
dc.identifier.elementsID673289
dc.identifier.issueNo10
dc.identifier.paperNo3178
dc.identifier.urihttps://doi.org/10.3390/s25103178
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23899
dc.identifier.volumeNo25
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/1424-8220/25/10/3178
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.subjectEEG generative models
dc.subjectvariational autoencoders
dc.subjectgenerative adversarial networks
dc.subjectdiffusion models
dc.subjectbrain-computer interfaces
dc.titleVirtual electroencephalogram acquisition: a review on electroencephalogram generative methods
dc.typeArticle
dcterms.dateAccepted2025-05-08

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