What Causes Performance Degradation in Cross-Subject EEG Classification?
For EEG researchers, this work clarifies the underlying mechanisms of cross-subject performance drops, highlighting the need for appropriate evaluation protocols.
This paper identifies two main causes of performance degradation in cross-subject EEG classification: inter-subject variability for multi-class-per-subject tasks and shortcut learning for single-class-per-subject tasks, providing systematic experimental evidence.
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.