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Related Experiment Video

Updated: Jan 9, 2026

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

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A Two-Stage Deep Learning Approach for EEG Artifact Removal and Classification: Towards Reliable Wearable

Andrea Farabbi, Filippo Ballabio, Matteo Rossi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

Summary

This summary is machine-generated.

This study introduces a novel two-stage system for removing and classifying electroencephalography (EEG) artifacts. The approach accurately identifies ocular artifacts, enhancing neural signal processing for wearable devices.

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Area of Science:

Neuroscience
Signal Processing
Machine Learning

Background:

Electroencephalography (EEG) artifact removal is crucial for accurate neural signal processing.
Ocular artifacts, such as eye blinks and saccadic movements, significantly contaminate EEG data.
Existing methods often struggle with real-time artifact identification and removal, especially in specific brain regions.

Purpose of the Study:

To develop and evaluate a novel two-stage system for automated EEG artifact removal and classification.
To improve the accuracy of artifact removal in temporal and frontal EEG recordings.
To enable reliable artifact identification for continuous monitoring and Brain-Computer Interface (BCI) applications.

Main Methods:

A two-stage deep learning approach combining a modified IC-UNet for artifact removal and a modified VGGNet for artifact classification.
Parallel encoding paths with channel-specific feature extraction in the denoising network.
Automatic triggering of the classification stage based on signal difference thresholds.

Main Results:

The denoising network achieved high correlation coefficients between predicted and ground truth signals in temporal (T5: 0.86, T6: 0.85) and frontal (F3: 0.83) regions.
The classification network demonstrated excellent performance with 99.35% accuracy, correctly classifying 616 out of 620 cases.
The system effectively identified ocular artifacts, including eye blinks and saccadic movements.

Conclusions:

The proposed two-stage system offers a feasible and accurate solution for EEG artifact removal and classification.
This method is particularly relevant for temporal and behind-the-ear EEG recordings, crucial for wearable EEG devices.
The findings support the development of advanced hybrid BCI systems and continuous EEG monitoring solutions.