Motor Imagery Classification Enhancement using Generative Adversarial Networks for EEG Spectrum Image Generation
Author:
Affiliation:
1. Ain Shams University,Computer and Systems Engineering Department,Cairo,Egypt
2. Technical Research Center,Biomedical Engineering Department,Cairo,Egypt
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10178426/10178704/10178849.pdf?arnumber=10178849
Reference29 articles.
1. Towards EEG Generation Using GANs for BCI Applications
2. Augmenting The Size of EEG datasets Using Generative Adversarial Networks
3. WGAN Domain Adaptation for EEG-Based Emotion Recognition
4. Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface
5. Generative adversarial networks
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1. Toward calibration-free motor imagery brain–computer interfaces: a VGG-based convolutional neural network and WGAN approach;Journal of Neural Engineering;2024-07-31
2. On the role of generative artificial intelligence in the development of brain-computer interfaces;BMC Biomedical Engineering;2024-05-02
3. Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model;Cerebral Cortex;2024-01-05
4. Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey;IEEE Access;2024
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