这项研究展示了如何通过参数高效的自适应，在有限的计算和数据收集负担下，有效地部署EEG基础模型（EEG-FM），值得一看。
研究参数高效的自监督适应，仅更新9%的参数即可对齐表示以适应目标任务。在两个模型BIOT和CBraMod上评估，在三个临床EEG数据集上测试，表现优于线性探测，AUCPR提升20倍。固定计算预算下，峰值性能仅需20-50%的无标签数据。性能与患者数量无关，仅依赖于时间窗口的多样性。代码可在GitHub获取。
Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets
EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tuning is impractical for resource-constrained clinical settings due to high computational requirements. In this work, we investigate whether parameter-efficient self-supervised adaptation, updating only 9% of parameters suffices to align representations to target tasks. We evaluate our method on two state-of-the-art models with different pretraining objectives: BIOT (contrastive) and CBraMod (masked reconstruction), and evaluate on three clinical EEG datasets for abnormality detection (TUAB), event classification (TUEV), and seizure detection (CHB-MIT) under both in-distribution and out-of-distribution conditions. SSL adaptation yields consistent gains over linear probing, up to 20x AUCPR. Under a fixed compute budget, peak performance requires only 20--50% of available unlabeled data. Critically, when total window count is fixed, performance remains invariant to patient count, suggesting that performance is dependent on overall temporal window diversity only. Our findings demonstrate that parameter-efficient adaptation enables effective deployment of EEG Foundation models (EEG-FM) with minimal computational overhead and data collection burden. Code available at: https://github.com/c3n-group/efficient-eeg-adapt