Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable or tested whether reconstruction metrics predict downstream neural-signal utility. We address both gaps by fixing architecture, loss, data split, and training recipe while sweeping only channel width from 1.05K to 40.26K parameters in a minimal depthwise-separable convolutional U-Net. Models were evaluated on the EEGDenoiseNet benchmark, cross-dataset BCI transfer tests, controlled baseline retraining, and downstream motor-imagery classification with five decoder families across all nine BCI Competition IV-2a subjects. Reconstruction performance saturated by 3-6.5K parameters, with post-elbow gains of at most 0.015 correlation coefficient per log10-parameter unit. An 8.46M-parameter baseline retrained under the same pipeline matched the 40.26K compact variant on EOG--a 200x parameter gap yielding no advantage--while a Patch-Transformer control reproduced the same diminishing-return shape. Downstream evaluation exposed a classifier-dependent metric-utility gap: reconstruction-optimized denoising significantly degraded CSP+LDA classification across all nine subjects and three artifact types (best denoised accuracy 0.547 vs. 0.612 noisy baseline; Bonferroni p=0.0488), persisting on naturally recorded trials (Delta=-0.047; BH-FDR q=0.0049). End-to-end neural decoders showed variable or neutral effects. Standard EEG denoising benchmarks are saturated far below current model capacity, and reconstruction metrics do not predict BCI utility. Ultra-compact models at 33-46 KB and 1.27-2.61M FLOPs/segment are practical for edge deployment. These findings argue for capacity-controlled evaluation, harder task-aware benchmarks, and mandatory downstream validation.

翻译：深度学习脑电去噪架构已从数万参数扩展至数千万参数，但尚无研究将模型容量作为实验变量进行分离，也未验证重建指标能否预测下游神经信号效用。我们通过固定架构、损失函数、数据划分及训练方案，仅在最小深度可分离卷积U-Net中将通道宽度从1.05K参数扫描至40.26K参数，填补了这两项空白。模型在EEGDenoiseNet基准、跨数据集BCI迁移测试、受控基线再训练，以及涵盖全部九个BCI竞赛IV-2a被试的五种解码器家族的下游运动想象分类上进行了评估。重建性能在3-6.5K参数处达到饱和，拐点后每对数参数单位的相关性系数增量最多为0.015。在同一流程下再训练的8.46M参数基线模型与40.26K紧凑变体在EOG上表现一致——200倍参数差距未带来任何优势——而Patch-Transformer对照组复现了相同的收益递减形态。下游评估揭示了依赖解码器的度量-效用差距：经过重建优化的去噪处理显著降低了全部九个被试及三种伪迹类型的CSP+LDA分类性能（最佳去噪准确率0.547 vs. 噪声基线0.612；Bonferroni校正p=0.0488），且在自然采集试次中持续存在（Delta=-0.047；BH-FDR q=0.0049）。端到端神经解码器表现出可变的或中性的效应。标准脑电去噪基准在远低于当前模型容量时即已饱和，且重建指标无法预测BCI效用。33-46 KB、1.27-2.61M FLOPs/片段的超紧凑模型具有边缘部署的实用性。这些发现主张采用容量受控的评估、更具挑战性的任务感知基准以及强制性的下游验证。