Therefore, the use of data augmentation which consists of adding batches of data with patterns quite similar to the training data can offer an interesting solution. Inspired by the successes of the generative a
Using a random signal in a GAN, it is not possible to study the question of whether it is possible to predict Nogo trial neurophysiological activity using information from Go trials (and vice versa). For that reason, we chose to use EEG data from a well-defined experimental paradigm ...
It is worth mentioning that, due to the limited size of our dataset, we attempted to apply three data augmentation techniques: noise injection (NI), conditional variational autoencoder (cVAE), and conditional GAN with Wasserstein price function and gradient penalty (cWGAN-GP) to add new artificia...
employing RGAN for augmentation using only 25% of the available dataset showed that the DNN performance was remarkably improved by 36% compared to its performance without RGAN-generated data. In addition, DNN trained using RGAN-generated data from 25% of the training dataset was 13.8% and 7.1...
EEG data augmentation for emotion recognition with a multiple generator conditional Wasserstein GANEEGEmotion recognitionGANData augmentationEEG-based emotion recognition has attracted substantial attention from researchers due to its extensive application prospects, and substantial progress has been made in ...
This study presents a novel approach using Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic electroencephalography (EEG) and electrocardiogram (ECG) waveforms. The synthetic EEG data represent concentration and relaxation mental states, while the synthetic ...
EEG data augmentation for emotion recognition using a conditional Wasserstein GAN. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 17–21 July 2018; pp. 2535–2538. [Google Scholar] [CrossRef] Bird, ...
Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks. arXiv 2017, arXiv:1704.00849. [Google Scholar] Razghandi, M.; Zhou, H.; Erol-Kantarci, M.; Turgut, D. Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation...