Boosting EEG and ECG Classification with Synthetic Biophysical Data Generated via Generative Adversarial Networks

Boosting EEG and ECG Classification with Synthetic Biophysical Data Generated via Generative Adversarial Networks

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 synt...

Full description

Saved in:
Bibliographic Details
Main Authors: Archana Venugopal, Diego Resende Faria
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Applied Sciences
Subjects:
synthetic data generation
GAN
WGAN-GP
EEG
ECG
Online Access:https://www.mdpi.com/2076-3417/14/23/10818
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items