Cross-subject EEG classification typically achieves significantly lower performance than subject-dependent settings. Although this phenomenon has been widely observed in the literature, the underlying causes have not been systematically studied. In this paper, we design a series of controlled experiments to investigate the mechanisms behind the performance drop in cross-subject EEG classification across different EEG tasks. We show that the performance degradation can generally be attributed to two factors: inter-subject variability and shortcut learning. Specifically, multi-class-per-subject EEG classification tasks, such as motor imagery, emotion recognition, and ERP stimulus classification, are mainly affected by inter-subject variability, whereas single-class-per-subject EEG classification tasks, such as brain disease detection, are primarily influenced by shortcut learning based on subject-specific features. These findings provide new insights into the challenges of cross-subject EEG classification and emphasize the importance of appropriate evaluation protocols in EEG research. The code is available at https://github.com/DL4mHealth/EEG-Cross-Subject.

翻译：跨被试EEG分类通常比依赖特定被试的设置取得显著更低的性能。尽管这一现象已在文献中被广泛观察到，但其根本原因尚未得到系统研究。本文设计了一系列受控实验，以探究不同EEG任务中跨被试分类性能下降的机制。研究表明，性能下降通常可归因于两个因素：被试间变异性和捷径学习。具体而言，每个被试包含多类别的EEG分类任务（如运动想象、情绪识别和ERP刺激分类）主要受被试间变异性的影响，而每个被试仅包含单一类别的EEG分类任务（如脑疾病检测）则主要受基于被试特异性特征的捷径学习影响。这些发现为跨被试EEG分类的挑战提供了新见解，并强调了在EEG研究中采用恰当评估协议的重要性。代码发布在 https://github.com/DL4mHealth/EEG-Cross-Subject。