The quality of data augmentation serves as a critical determinant for the performance of contrastive learning in EEG tasks. Although this paradigm is promising for utilizing unlabeled data, static or random augmentation strategies often fail to preserve intrinsic information due to the non-stationarity of EEG signals where statistical properties change over time. To address this, we propose RL-BioAug, a framework that leverages a label-efficient reinforcement learning (RL) agent to autonomously determine optimal augmentation policies. While utilizing only a minimal fraction (10\%) of labeled data to guide the agent's policy, our method enables the encoder to learn robust representations in a strictly self-supervised manner. Experimental results demonstrate that RL-BioAug significantly outperforms the random selection strategy, achieving substantial improvements of 9.69\% and 8.80\% in Macro-F1 score on the Sleep-EDFX and CHB-MIT datasets, respectively. Notably, this agent mainly chose optimal strategies for each task -- for example, Time Masking with a 62\% probability for sleep stage classification and Crop \& Resize with a 77\% probability for seizure detection. Our framework suggests its potential to replace conventional heuristic-based augmentations and establish a new autonomous paradigm for data augmentation. The source code is available at \href{https://github.com/dlcjfgmlnasa/RL-BioAug}{https://github.com/dlcjfgmlnasa/RL-BioAug}.

翻译：数据增强的质量是决定对比学习在脑电任务中性能的关键因素。尽管该范式在利用未标记数据方面前景广阔，但由于脑电信号的非平稳性（其统计特性随时间变化），静态或随机的增强策略往往无法保留其内在信息。为解决这一问题，我们提出了RL-BioAug，一个利用标签高效强化学习智能体来自主确定最优增强策略的框架。该方法仅使用极小比例（10%）的标记数据来指导智能体的策略，使编码器能够以严格自监督的方式学习鲁棒表征。实验结果表明，RL-BioAug显著优于随机选择策略，在Sleep-EDFX和CHB-MIT数据集上的Macro-F1分数分别实现了9.69%和8.80%的大幅提升。值得注意的是，该智能体主要为每个任务选择了最优策略——例如，在睡眠分期任务中以62%的概率选择时间掩码，在癫痫检测任务中以77%的概率选择裁剪与缩放。我们的框架表明其有潜力取代传统的基于启发式的增强方法，并建立一种新的自主数据增强范式。源代码可在 \href{https://github.com/dlcjfgmlnasa/RL-BioAug}{https://github.com/dlcjfgmlnasa/RL-BioAug} 获取。