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Related Experiment Video

Updated: Jan 12, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

End-to-end EEG artifact removal method via nested generative adversarial network.

Tianqi Yang¹, Shengsheng Cai¹, Dongyang Xu^2,3,4

¹School of Electronics and Information Engineering, Soochow University, Suzhou 215006, People's Republic of China.

Biomedical Physics & Engineering Express

|November 3, 2025

Summary

This summary is machine-generated.

A novel nested generative adversarial network (GAN) effectively removes physiological artifacts from electroencephalogram (EEG) signals. This method enhances brain-computer interface (BCI) system performance by recovering clean EEG data.

Keywords:

electroencephalogram (EEG)end-to-end artifact removal nested generative adversarial network

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Area of Science:

Neuroscience
Signal Processing
Machine Learning

Background:

Physiological artifacts in EEG signals complicate analysis.
Effective artifact removal is crucial for reliable brain-computer interface (BCI) systems.

Purpose of the Study:

To propose an end-to-end EEG artifact removal method using a nested generative adversarial network (GAN).
To recover artifact-contaminated EEG signals for improved BCI performance.

Main Methods:

A nested GAN with inner (time-frequency) and outer (time) domains was developed.
A complex-valued restormer served as the generator for signal reconstruction.
Metric and multi-resolution discriminators were employed, with gradient balance for training stability.

Main Results:

The nested GAN achieved superior performance across realistic and semi-synthetic datasets.
Key metrics included MSE=0.098, PCC=0.892, RRMSE=0.065, ηtemporal=71.6%, and ηspectral=76.9%.
The method demonstrated robustness across various signal-to-noise ratio (SNR) levels.

Conclusions:

The proposed nested GAN offers an effective end-to-end solution for EEG artifact removal.
This advancement is expected to significantly contribute to the development of robust BCI systems.