Synthetic-Neuroscore: Using a neuro-AI interface for evaluating generative adversarial networks

Wang, Zhengwei; She, Qi; Smeaton, Alan F.; Ward, Tomás E.; Healy, Graham

doi:10.1016/j.neucom.2020.04.069

Cited by 11 publications

(6 citation statements)

References 45 publications

(73 reference statements)

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“…GANs have demonstrated success in augmenting EEG data for motor imagery, P300-based applications, emotion recognition, and epileptic seizure detection and prediction. It is noteworthy here that a few studies on EEG-based image generation have been excluded from this article such as [ 95 – 98 ], and [ 99 , 100 ]. The main justification for this elimination is that these studies mainly use GAN methods for image generation and EEG signals were being used as an auxiliary input without applying GAN to the EEG data itself.…”

Section: Discussionmentioning

confidence: 99%

Generative adversarial networks in EEG analysis: an overview

Habashi

Azab

Eldawlatly

et al. 2023

J NeuroEngineering Rehabil

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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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Section: Discussionmentioning

confidence: 99%

Generative adversarial networks in EEG analysis: an overview

Habashi

Azab

Eldawlatly

et al. 2023

J NeuroEngineering Rehabil

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“…More important, these images can be controlled to exhibit specific attributes (e.g. level of happiness or facial textures), and thus enhancing the stimuli variation in the experiment [73]. Data augmentation can be also helpful in the training of the GANs themselves [31,78], which allows to train them with only few examples and still get high quality generated data (e.g., images).…”

Section: Data Augmentation With Ganmentioning

confidence: 99%

Generative Adversarial Networks

Cohen¹,

Giryes²

2022

Preprint

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Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.

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“…Evaluation. A wide range of evaluation metrics has been proposed to evaluate the performance of GANs [12,13,14,15]. Current evaluations of GANs in computer vision are normally designed to consider two perspectives i.e., quality and quantity of generated data.…”

Section: Challengesmentioning

confidence: 99%

Generative adversarial networks in time series: A survey and taxonomy

Brophy,

Wang,

She

et al. 2021

Preprint

Self Cite

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Generative adversarial networks (GANs) studies have grown exponentially in the past few years. Their impact has been seen mainly in the computer vision field with realistic image and video manipulation, especially generation, making significant advancements. While these computer vision advances have garnered much attention, GAN applications have diversified across disciplines such as time series and sequence generation. As a relatively new niche for GANs, fieldwork is ongoing to develop high quality, diverse and private time series data. In this paper, we review GAN variants designed for time series related applications. We propose a taxonomy of discrete-variant GANs and continuous-variant GANs, in which GANs deal with discrete time series and continuous time series data. Here we showcase the latest and most popular literature in this field; their architectures, results, and applications. We also provide a list of the most popular evaluation metrics and their suitability across applications. Also presented is a discussion of privacy measures for these GANs and further protections and directions for dealing with sensitive data. We aim to frame clearly and concisely the latest and state-of-the-art research in this area and their applications to real-world technologies.

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Synthetic-Neuroscore: Using a neuro-AI interface for evaluating generative adversarial networks

Cited by 11 publications

References 45 publications

Generative adversarial networks in EEG analysis: an overview

Generative adversarial networks in EEG analysis: an overview

Generative Adversarial Networks

Generative adversarial networks in time series: A survey and taxonomy

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