There is major potential for using electroencephalography (EEG) in brain decoding that has been untapped due to the need for large amounts of data.
We found that augmenting EEG data enhanced classification performance for both neural networks and SVMs, but not for logistic regressions (see Figure 5).
In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as ...
We here use Generative Adversarial Networks (GANs) to create trial-level synthetic EEG samples. We can then use these samples as extra data to train whichever ...
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A novel generative adversarial network (GAN) model is proposed that can learn the statistical characteristics of the EEG signal and augment its datasets ...
In this study, we use conditional GANs for artificial gen- eration of raw EEG in order to augment the classification. The task is the detection of subject's ...
Jun 20, 2024 · We introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework.
Jun 21, 2024 · We introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework.
Apr 11, 2023 · This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals.
We here use Generative Adversarial Networks (GANs) to create trial-level synthetic EEG samples. We can then use these samples as extra data to train whichever ...