ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals

TL;DR

The paper introduces ART, a transformer-based model for effectively removing artifacts from multichannel EEG signals, improving the accuracy and reliability of neural data analysis in BCI applications.

Contribution

This study presents a novel transformer architecture for EEG artifact removal, utilizing enhanced data generation and comprehensive validation to outperform existing methods.

Findings

ART surpasses previous deep-learning artifact removal techniques.

The model improves signal quality for better source localization.

Enhanced training data generation boosts model performance.

Abstract

Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution for diverse artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments.