用压缩距离聚类方法，从脑电中提取稳定P300信号结构。
Algorithmic Clustering based on String Compression to Extract P300 Structure in EEG Signals
- 通过字符串压缩距离实现脑电信号聚类，抗个体与时间差异。
- 在两个数据集上表现接近顶尖方法，能有效识别P300特征。
- 可辅助选择关键电极，适合脑机接口信号分析场景。
P300是一种广泛应用于脑机接口的事件相关电位，但因其个体间和时间上的变异性，检测难度大。本文提出一种基于归一化压缩距离（NCD）的聚类方法，用于提取稳定的P300结构，具有强抗变异性能力。我们设计了一种新颖的信号到ASCII转换方式，生成适于压缩的表示对象，随后采用分层树状聚类与多维投影方法进行聚类分析。在两个公开数据集上的实验结果表明，该方法能有效揭示相关的P300结构，聚类性能与当前先进方法相当。此外，电极层面的分析显示，该方法有助于优化电极选择。这种以压缩驱动的聚类策略为脑电信号分析与P300识别提供了互补工具。
原文摘要 · Abstract (English)
P300 is an Event-Related Potential widely used in Brain-Computer Interfaces, but its detection is challenging due to inter-subject and temporal variability. This work introduces a clustering methodology based on Normalized Compression Distance (NCD) to extract the P300 structure, ensuring robustness against variability. We propose a novel signal-to-ASCII transformation to generate compression-friendly objects, which are then clustered using a hierarchical tree-based method and a multidimensional projection approach. Experimental results on two datasets demonstrate the method's ability to reveal relevant P300 structures, showing clustering performance comparable to state-of-the-art approaches. Furthermore, analysis at the electrode level suggests that the method could assist in electrode selection for P300 detection. This compression-driven clustering methodology offers a complementary tool for EEG analysis and P300 identification.
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