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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

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Single-channel EOG artifact removal using fixed frequency EWT and GMETV filter.

Jammisetty Yedukondalu¹, Kalyani Sunkara², Venkata Kishore Kumar Rejeti³

¹Department of Electronics and Communication Engineering, PACE Institute of Technology & Sciences, Ongole, 523272, AP, India.

Scientific Reports

|November 5, 2025

Summary

This study introduces an automated method using Fixed Frequency Empirical Wavelet Transform (FF-EWT) and a Generalized Moreau Envelope Total Variation (GMETV) filter to remove eye blink artifacts from single-channel EEG signals, improving brain signal analysis.

Keywords:

EEG EOG FF-EWT Feature extraction GMETV filter

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Area of Science:

Biomedical Engineering
Neuroscience
Signal Processing

Background:

Portable electroencephalogram (EEG) systems offer user-friendly, wearable solutions for healthcare.
Electrooculogram (EOG) artifacts, particularly from eye blinks, significantly hinder accurate diagnosis in single-channel EEG (scEEG).
Effective artifact removal is crucial for reliable scEEG data analysis.

Purpose of the Study:

To develop and validate an automated method for removing EOG artifacts from scEEG signals.
To enhance the accuracy of brain signal analysis in wearable EEG systems.
To provide an effective preprocessing tool for clinical and research applications.

Main Methods:

An automated artifact removal technique combining Fixed Frequency Empirical Wavelet Transform (FF-EWT) with a Generalized Moreau Envelope Total Variation (GMETV) filter.
Identification of artifact-contaminated components using kurtosis (KS), dispersion entropy (DisEn), and power spectral density (PSD) metrics.
Validation using both synthetic and real-world EEG datasets.

Main Results:

The FF-EWT+GMETV method successfully suppressed EOG artifacts while preserving essential low-frequency EEG information.
Synthetic data showed lower Relative Root Mean Square Error (RRMSE) and higher Correlation Coefficient (CC).
Real EEG recordings demonstrated improved Signal-to-Artifact Ratio (SAR) and Mean Absolute Error (MAE).

Conclusions:

The proposed FF-EWT+GMETV technique offers a robust solution for EOG artifact removal in scEEG.
This advancement significantly improves the quality of brain signal analysis.
The method is a valuable preprocessing tool for clinical diagnostics and neuroscience research.