ArticleFrontiers in neuroinformatics2020
EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss.
Article in Frontiers in neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification.Brain informatics · 2026Article
- Missing Data Gap Imputation Methods in Electroencephalogram (EEG) Signals: A Systematic Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches.AIMS neuroscience · 2026Review
- Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.Sensors (Basel, Switzerland) · 2025Review
- Generative AI Models in Time-Varying Biomedical Data: Scoping Review.Journal of medical Internet research · 2025Article
- Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches.Frontiers in human neuroscience · 2025Article
- Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning.PeerJ. Computer science · 2025Article
- AnEEG: leveraging deep learning for effective artifact removal in EEG data.Scientific reports · 2024Article
- On the role of generative artificial intelligence in the development of brain-computer interfaces.BMC biomedical engineering · 2024Review
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Generating synthetic gait patterns based on benchmark datasets for controlling prosthetic legs.Journal of neuroengineering and rehabilitation · 2023Article
- Generative Adversarial Network (GAN) for Simulating Electroencephalography.Brain topography · 2023Article
- MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals.Bioengineering (Basel, Switzerland) · 2023Article
- Generative adversarial networks in EEG analysis: an overview.Journal of neuroengineering and rehabilitation · 2023Review
- Article
- Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer's Disease Subjects.Life (Basel, Switzerland) · 2022Article
- E2SGAN: EEG-to-SEEG translation with generative adversarial networks.Frontiers in neuroscience · 2022Article
- Synthetic Epileptic Brain Activities with TripleGAN.Computational and mathematical methods in medicine · 2022Article
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5 authors.
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Abstract
Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorithms lead to loss of the representative details of brain activity and suffer from remaining artifacts because such algorithms only aim to minimize the temporal mean-squared-error (MSE) under generic penalties. Instead of using temporal MSE according to conventional mathematical models, this paper introduces a novel reconstruction algorithm based on generative adversarial networks with the Wasserstein distance (WGAN) and a temporal-spatial-frequency (TSF-MSE) loss function. The carefully designed TSF-MSE-based loss function reconstructs signals by computing the MSE from time-series features, common spatial pattern features, and power spectral density features. Promising reconstruction and classification results are obtained from three motor-related EEG signal datasets with different sampling rates and sensitivities. Our proposed method significantly improves classification performances of EEG signals reconstructions with the same sensitivity and the average classification accuracy improvements of EEG signals reconstruction with different sensitivities. By introducing the WGAN reconstruction model with TSF-MSE loss function, the proposed method is beneficial for the requirements of high classification performance and low cost and is convenient for the design of high-performance brain computer interface systems.
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