通过加权筛选可靠脑电试次，提升解码精度。
Informed Bootstrap Augmentation Improves EEG Decoding
- 根据脑电成分差异计算试次权重，优先选择可靠样本
- 在句子评估任务中准确率从68.35%提升至71.25%
- 适合脑电信号解码、小样本场景下的数据增强
脑电图（EEG）可提供神经动态的精细信息，但受限于噪声和试次间变异，导致数据稀缺或复杂范式下解码性能受限。数据增强常用于改善特征表示，但传统均匀平均忽略了试次的有用性差异，可能损害表征质量。本文提出一种基于可靠性的加权引导采样方法，通过相对事件相关电位（ERP）差异计算试次权重，在概率采样与平均中优先使用更可靠的试次。在句子评估范式中，该方法将解码准确率最高提升至71.25%，优于未加权方法的68.35%。结果表明，基于可靠性增强的样本能生成更鲁棒、更具判别力的脑电表征。代码已公开于https://github.com/lyricists/NeuroBootstrap。
原文摘要 · Abstract (English)
Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restricted or complex paradigms. Data augmentation is often employed to enhance feature representations, yet conventional uniform averaging overlooks differences in trial informativeness and can degrade representational quality. We introduce a weighted bootstrapping approach that prioritizes more reliable trials to generate higher-quality augmented samples. In a Sentence Evaluation paradigm, weights were computed from relative ERP differences and applied during probabilistic sampling and averaging. Across conditions, weighted bootstrapping improved decoding accuracy relative to unweighted (from 68.35% to 71.25% at best), demonstrating that emphasizing reliable trials strengthens representational quality. The results demonstrate that reliability-based augmentation yields more robust and discriminative EEG representations. The code is publicly available at https://github.com/lyricists/NeuroBootstrap.
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