# 面向EEG基础模型的测试时自适应：真实分布偏移下的系统研究

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-18 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmoca5rxt02d2slsjkprp4plu
- 原文链接：https://arxiv.org/abs/2604.16926

## AI 摘要

研究团队发布NeuroAdapt-Bench基准，系统评估测试时自适应（TTA）在EEG基础模型上的实际表现。实验覆盖多种预训练模型、下游任务及异构数据集（含Ear-EEG等极端模态偏移）。结果显示，标准TTA方法性能提升不稳定且常导致模型退化，梯度方法退化尤为严重；而无优化方法表现出更强稳定性和可靠性。该研究揭示了现有TTA技术在脑电信号处理中的局限性，强调需开发领域特定的自适应策略。

## 正文

Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations. Test-time adaptation (TTA) offers a promising solution by enabling models to adapt to unlabeled target data during inference without access to source data, a valuable property in healthcare settings constrained by privacy regulations and limited labeled data. However, its effectiveness for EEG remains largely underexplored. In this work, we introduce NeuroAdapt-Bench, a systematic benchmark for evaluating test-time adaptation methods on EEG foundation models under realistic distribution shifts. We evaluate representative TTA approaches from other domains across multiple pretrained foundation models, diverse downstream tasks, and heterogeneous datasets spanning in-distribution, out-of-distribution, and extreme modality shifts (e.g., Ear-EEG). Our results show that standard TTA methods yield inconsistent gains and often degrade performance, with gradient-based approaches particularly prone to heavy degradation. In contrast, optimization-free methods demonstrate greater stability and more reliable improvements. These findings highlight the limitations of existing TTA techniques in EEG, provide guidance for future development, and underscore the need for domain-specific adaptation strategies.