arXiv：cs.LG· Haochen Chai, Qixu Zhu, Siyao Li, Fangfang Jiang·· 4 小时前AI 评分34
去除伪迹改善了皮肤电波形，却未提升 VR 平衡任务的下游分类
Artifact removal improves electrodermal waveforms but not downstream classification in a virtual-reality balance task
AI 导读
研究在虚拟现实平衡扰动任务中检验了伪迹去除能否提升皮肤电活动（EDA）分类：基于专家校正 EDA 训练并冻结的残差门控网络，在基准集上检出伪迹的中位 AUROC 为 0.94，伪迹区域误差降低 17.8%，但用于 VR 录制数据时未改善分类。
正文
Abstract:Artifact removal routinely precedes the classification of electrodermal activity (EDA), on the assumption that a cleaner signal supports a better decision. We tested this assumption in a virtual-reality (VR) balance-disturbance task. A residual gating network was trained on a benchmark with expert-corrected EDA, frozen, and applied to VR recordings, where raw and gated signals were classified by five published time-series methods under identical leave-one-participant-out evaluation. On the benchmark the gate detected artifacts well (median record AUROC 0.94) and reduced error inside artifact regions by 17.8%. In the VR task it did not improve classification. Changes in balanced accuracy ranged from -1.35 to +0.93 percentage points, no classifier improved and two lost accuracy, and all five were equivalent to raw input within +/- 3.32 points. The benefit was lost between waveform and decision. The correction that lowered waveform error also reduced skin conductance response detection in all 43 benchmark records. Processing left 92.8% of predictions unchanged, and the predictions it did change were corrected and corrupted at similar rates. The VR recordings also carried little contamination (an estimated 4.6% of samples), and even perfect localization of deliberately injected artifacts recovered only 3.3 points in the most sensitive classifier. A pooled association between artifact level and accuracy (11.3 points) disappeared within participants (0.1 points), showing how differences between people can make cleaning look useful. Preprocessing should be judged by the decision it supports, against an unprocessed arm.
| Comments: | 10 pages, 9 figures, 3 tables. Code and frozen data: this https URL |
| Subjects: | Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Signal Processing (eess.SP) |
| ACM classes: | J.3; I.5.4 |
| Cite as: | arXiv:2610.07438 [cs.HC] |
| (or arXiv:2610.07438v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07438 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haochen Chai [view email]
[v1]
Mon, 5 Oct 2026 21:42:17 UTC (511 KB)
来源：arXiv：cs.LG · arxiv.org