Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning

TL;DR

The paper introduces iPSD, a self-supervised deep learning method for EEG denoising that does not require clean data, effectively handling low SNR and complex artifacts in wearable EEGs.

Contribution

iPSD enables unsupervised training of EEG denoisers by partitioning noisy segments into independent realizations, eliminating the need for clean references.

Findings

iPSD outperforms existing methods at SNRs as low as -10 dB.

It maintains high spectral fidelity in challenging artifact conditions.

Validated on wearable in-ear EEG sensors.

Abstract

Denoising wearable electroencephalogram (EEG) is inherently challenging since neural activity is not only subtle but also inseparable from spectrally overlapping noise artifacts. Classical signal processing methods, relying on fixed or heuristic rules, cannot handle the time-varying pervasive artifacts in wearable EEGs. Deep learning methods, on the other hand, show promise in decomposition-free EEG denoising using highly expressive neural networks, but the training requires artifact-free EEG, which is inherently unobtainable. To address this, we propose Intelligent Partitioning for Self-supervised Denoising (iPSD). Our method eliminates the need for clean references by learning to partition an input EEG segment into independent noisy realizations with the same underlying signal. This enables self-supervision of deep learning denoisers, even in zero-shot settings where only a single EEG segment to be denoised is available. We validate iPSD through extensive experiments, including validations on wearable EEG from in-ear sensors. The results show that iPSD achieves state-of-the-art performance, most notably under extremely low signal-to-noise ratios (down to -10 dB) and challenging artifacts (e.g., EMG), with spectral fidelity orders of magnitude higher than competitive baselines.