Calibration-free Ocular artifact reduction in EEG signals using a montage-independent deep learning model

Ocular artifacts (OA) are the most common artifacts in electroencephalography (EEG), significantly affecting signal quality and analysis. Common approaches like indepentent component analysis (ICA) or regression-based methods address this problem but require several minutes of subject-specific EEG and electrooculography (EOG) calibration, making them impractical for real-time applications like brain–computer interfaces (BCI). In this study, we introduce EEGOAR-Net, a deep learning architecture designed to reduce OA in EEG. It address these issues while also providing flexibility across various EEG montages. Based on U-Net architecture, EEGOAR-Net was trained with contaminated EEG signals in order to reconstruct them with OA attenuated, using SGEYESUB as the reference method. In addition, a novel training methodology based on masking signals from different channels was applied to make EEGOAR-Net independent of the EEG montage used. A cross-validation analysis was conducted to assess EEGOAR-Net’s performance, demonstrating its ability to reduce EEG-EOG correlations to chance levels across most brain regions with minimal information loss. Thus, the performance of EEGOAR-Net is comparable to that of the reference method without the need for subject-specific calibration or EOG channels. Furthermore, validation on an additional dataset confirmed effective blink reduction and superior preservation of neural information compared to the state-of-the-art models: 1D-ResCNN and IC-U-Net. EEGOAR-Net’s performance across datasets and versatility across montages prove it to be a reliable and practical solution for EEG-based research and BCI applications, ensuring a notable reduction of OA on signal while maintaining the integrity of neural information. • A novel deep-learning model is proposed and validated to reduce EEG eye artifacts. • We present a new training method that makes EEG-based models montage-independent • EEGOAR-Net works across subjects and montages, with no calibration or EOG needed.