2023
DOI: 10.1186/s12984-023-01169-w
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Generative adversarial networks in EEG analysis: an overview

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… Show more

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Cited by 34 publications
(18 citation statements)
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“…Based on this notion, other types of well-known AEs, such as stacked AE [30] and variational AE (VAE) [31] have been derived. In recent years, there has been a growing interest in utilizing GANs for EEG data augmentation [32], inspired by their well-established success in image and sound data synthesis. However, generating synthetic EEG signal sequences using GANs necessitates the development of specialized techniques and raises certain concerns that need to be addressed.…”
Section: Related Workmentioning
confidence: 99%
“…Based on this notion, other types of well-known AEs, such as stacked AE [30] and variational AE (VAE) [31] have been derived. In recent years, there has been a growing interest in utilizing GANs for EEG data augmentation [32], inspired by their well-established success in image and sound data synthesis. However, generating synthetic EEG signal sequences using GANs necessitates the development of specialized techniques and raises certain concerns that need to be addressed.…”
Section: Related Workmentioning
confidence: 99%
“…Data augmentation has been successfully applied to epileptic seizure prediction from EEG data [21], and to delineate the seizure-onset zone in individuals with focal epilepsy [86]. See Habashi, Azab, et al [87] for a full review. In genomics, Wei, Li, et al [84] used data augmentation of genetic cancer data to demonstrate how this strategy improves cancer classification.…”
Section: Challenges and Potential Solutionsmentioning
confidence: 99%
“…Although our previous studies utilizing the shared data revealed the neural mechanisms 1 , 5 , 6 and successfully trained the deep learning algorithms 6 , 22 , the dataset may not be sufficient for training super-giant AI models like the generative pre-trained transformer (GPT). The issue of data scarcity can be partially overcome by data augmentation 33 , 34 , generation 35 , and transfer learning 36 techniques. Data augmentation involves slight modifications to the existing data to increase its quantity, achieved through methods such as noise addition, sliding windows, and recombination of segmentation.…”
Section: Technical Validationmentioning
confidence: 99%