A Gated Artifact Management Pipeline for Low-Density Eyewear EEG

To develop an artifact-management method suitable for low-density, dry-electrode EEG recorded from a wearable eyewear platform, enabling artifact detection, classification, and correction while avoiding unnecessary processing of clean neural activity.

A gated two-stage pipeline is proposed in which a lightweight gate classifier first identifies artifact-contaminated EEG windows and selectively triggers further processing only when needed. The complete pipeline combines: (i) a time-domain feature-based detector, (ii) a convolutional-recurrent network for three-class artifact discrimination (eye blinks, horizontal eye movements, facial movements), and (iii) a UNet-based denoising autoencoder.

Evaluated on the eyewear EEG dataset (29 subjects) using a leave-one-subject-out scheme, the gate classifier achieved a balanced accuracy of 0.89. The artifact classifier reached a balanced accuracy of 0.84 with a median macro-F1 of 0.83. The denoiser achieved a median normalized RMSE lower than 0.20 and a spectral cosine similarity above 0.84. In the end-to-end evaluation, the pipeline achieved a median normalized RMSE of 0.19 and a spectral cosine similarity of 0.89. Evaluation on an independent external benchmark showed that the proposed pipeline maintained a slightly lower gate detection performance (balanced accuracy 0.82) and achieved higher two-class classification performance. The denoising performance was comparable to that obtained on the dataset collected for this study, with absolute median differences not exceeding 0.04 for both normalized RMSE and spectral cosine similarity.

These results support the effectiveness of the proposed approach for artifact management in wearable eyewear EEG, while maintaining a design compatible with resource-constrained wearable implementations.