mlsanityData to 7 Oct 2026

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

arXiv 2409.07326 ↗First posted 11 Sep 2024eess.SP cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 77 references that are in the map. First posted on arXiv 11 Sep 2024.

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27 of 77 references.

References · 27

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15 more
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