On the use of generative deep neural networks to synthesize artificial multichannel EEG signals
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Abstract (arXiv)
Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering. In this paper we firstly review recently emerging studies on generating artificial electroencephalography (EEG) signals with deep neural networks. Subsequently, we present our feasibility experiments on generating condition-specific multichannel EEG signals using conditional variational autoencoders. By manipulating real resting-state EEG epochs, we present an approach to synthetically generate time-series multichannel signals that show spectro-temporal EEG patterns which are expected to be observed during distinct motor imagery conditions.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 16 Feb 2021.
Builds on
8 of 24 references.
References · 8
- 2013Auto-Encoding Variational Bayescited by 4,053
- 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
- 2018EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalscited by 48
- 2020Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Modelscited by 21
- 2019Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classificationcited by 12
- 2018Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoderscited by 11
- 2018Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networkscited by 8
- 2019Synthetic Epileptic Brain Activities Using Generative Adversarial Networkscited by 4
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.
2 papers.