SPAR-EEG: Selective Pass-wise Artifact Reduction for Wearable Single-channel EEG Denoising

Abstract

Single-channel electroencephalography (EEG) is attractive for wearable neurotechnology and brain-computer interface (BCI) applications, including assistive interfaces and clinical monitoring, but artifact suppression is difficult when auxiliary channels, artifact labels, or user-selected clean baseline segments are unavailable. We introduce SPAR-EEG, a self-contained framework for Selective Pass-wise Artifact Reduction in single-channel EEG. The framework applies three artifactspecific attenuation passes to each EEG epoch: a variational mode decomposition (VMD)-based pass for high-frequency electromyo-graphic (EMG) bursts, a singular spectrum analysis (SSA)-based pass for blink-like electrooculographic (EOG) transients, and an SSA-based pass for slow motion-related drift. Rather than rejecting components globally, each pass estimates artifact-dominant regions and attenuation strength directly from the input channel. SPAR-EEG was evaluated using controlled EEGdenoiseNet and PhysioBank benchmarks, pass ablations, task-locked event-related potential (ERP) preservation, runtime diagnostics, dry-electrode exercise EEG, and a downstream rapid serial visual presentation (RSVP)/P300 speller task using only FP1 and FP2. Across 26 EEGdenoiseNet input signal-to-noise ratio (SNR) levels, it obtained the largest average artifact-region SNR improvement among the tested wavelet, empirical mode decomposition (EMD), and artifact-label-guided wavelet quantile normalization (WQN) baselines for EMG, EOG, and combined EOG+EMG contamination (9.05, 8.28, and 8.00 dB, respectively). In exercise EEG, denoising reduced high-amplitude artifact burden and increased alpha and steady-state visual evoked potential (SSVEP) spectral-prominence metrics. In the P300 validation, the full SPAR-EEG sequence increased repetition-curve area under the curve (AUC) by 0.048 (Holm-adjusted p = 0.0069) and improved final Letter@15 accuracy by 7.8 percentage points. These results suggest that artifact-specific selective attenuation can provide a practical self-contained alternative for single-channel EEG denoising in low-burden and movement-prone settings.