用相对时间偏移预测提升脑电图长程依赖建模能力
Learning the relative composition of EEG signals using pairwise relative shift pretraining
- 设计新预训练任务，通过预测脑电窗对的相对时间偏移来捕捉长期依赖
- 在睡眠分期等任务中，标签效率和迁移学习性能均优于现有方法
- 适合需要少标注数据的临床脑电信号分析场景
自监督学习（SSL）为从无标签脑电图（EEG）数据中学习表征提供了有前景的途径，可减少睡眠分期、癫痫检测等临床应用中对昂贵标注的需求。尽管当前主流的EEG SSL方法多采用掩码重建策略（如掩码自编码器，MAE），侧重于局部时序模式的恢复，但位置预测预训练仍被忽视，其实其具备学习神经信号中长程依赖的潜力。本文提出一种新型预训练任务——成对相对偏移（PAIRwise Relative Shift, PARS），通过预测随机采样的脑电窗对之间的相对时间偏移来实现。与基于重建的方法不同，PARS促使编码器捕捉神经信号中的相对时序组成和长程依赖。在多种脑电解码任务上的全面评估表明，经PARS预训练的变压器模型在标签效率和迁移学习设置下持续优于现有预训练策略，确立了自监督脑电表示学习的新范式。
原文摘要 · Abstract (English)
Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data, reducing the need for expensive annotations for clinical applications like sleep staging and seizure detection. While current EEG SSL methods predominantly use masked reconstruction strategies like masked autoencoders (MAE) that capture local temporal patterns, position prediction pretraining remains underexplored despite its potential to learn long-range dependencies in neural signals. We introduce PAirwise Relative Shift or PARS pretraining, a novel pretext task that predicts relative temporal shifts between randomly sampled EEG window pairs. Unlike reconstruction-based methods that focus on local pattern recovery, PARS encourages encoders to capture relative temporal composition and long-range dependencies inherent in neural signals. Through comprehensive evaluation on various EEG decoding tasks, we demonstrate that PARS-pretrained transformers consistently outperform existing pretraining strategies in label-efficient and transfer learning settings, establishing a new paradigm for self-supervised EEG representation learning.
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