Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets

2026-08-25

AI Summary

研究通过仅更新9%参数的自监督适应方法，解决EEG基础模型在临床数据集上泛化能力有限的问题，减少计算和数据需求。

Abstract

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

Problem

• parameter-efficient
• self-supervised adaptation
• EEG foundation models
• clinical datasets
• fixed computational budgets

Innovation

• Parameter-Efficient Adaptation
• Self-Supervised Learning
• EEG Foundation Models
• Temporal Window Diversity