SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG

TL;DR

This paper introduces SSDLabeler, a framework that generates realistic semi-synthetic EEG data with multiple artifacts for improved multi-label artifact classification, addressing limitations of prior methods.

Contribution

SSDLabeler innovatively combines ICA decomposition, artifact verification, and multi-artifact reinjection to produce more realistic semi-synthetic EEG data for training classifiers.

Findings

Enhanced classification accuracy on raw EEG data.

Better representation of artifact co-occurrence and complexity.

Scalable approach for artifact handling in EEG analysis.

Abstract

EEG recordings are inherently contaminated by artifacts such as ocular, muscular, and environmental noise, which obscure neural activity and complicate preprocessing. Artifact classification offers advantages in stability and transparency, providing a viable alternative to ICA-based methods that enable flexible use alongside human inspections and across various applications. However, artifact classification is limited by its training data as it requires extensive manual labeling, which cannot fully cover the diversity of real-world EEG. Semi-synthetic data (SSD) methods have been proposed to address this limitation, but prior approaches typically injected single artifact types using ICA components or required separately recorded artifact signals, reducing both the realism of the generated data and the applicability of the method. To overcome these issues, we introduce SSDLabeler, a framework that generates realistic, annotated SSDs by decomposing real EEG with ICA, epoch-level artifact verification using RMS and PSD criteria, and reinjecting multiple artifact types into clean data. When applied to train a multi-label artifact classifier, it improved accuracy on raw EEG across diverse conditions compared to prior SSD and raw EEG training, establishing a scalable foundation for artifact handling that captures the co-occurrence and complexity of real EEG.