单通道 EEG 去噪与伪迹去除：方法、基准测试与可复现性系统综述

Objective. Single-channel and wearable electroencephalography (EEG) reduce acquisition burden but limit the spatial information available to conventional multichannel artifact-removal methods. We conducted a systematic scoping review of single-channel-compatible EEG artifact removal, linking method operation with artifact coverage, evaluation design, generalization, deployment, comparison, and reproducibility. Approach. Database and citation searches through 15 2026 identified 178 articles describing 199 method variants. For learned methods, 303 reported model-dataset evaluations were also examined for data independence, generalization, and deployment. Main results. Most variants were reference-free at inference (179/199), while 13 used a non-neural auxiliary reference. Methods spanned seven operating paradigms, including decomposition, transformed source separation, adaptive correction, and learned reconstruction. Ocular and muscle artifacts were evaluated most often, and 38/178 articles tested simultaneous or overlapping mixtures. Across 380 dataset uses, evaluation centered on controlled signal reconstruction. Known-target fidelity was reported for 170/199 variants, compared with 21 for clean-input or non-artifact signal comparison, 32 for contaminated downstream utility, and three for clean-input downstream non-degradation. Among 81 learned variants, 18 reported source-trained application to an external dataset, device, or site; only 31/303 evaluations explicitly separated test windows or epochs, clean sources, and artifact sources from training. No measured deployment evidence was reported for 122/199 variants. Public method resources were available for 21 variants, while trained weights and explicit licenses were uncommon. Significance. The review provides a framework for selecting datasets, metrics, comparators, and implementations, and for distinguishing reconstruction, neural preservation, transfer, and wearable deployment evidence