Evaluating spatial normalization for SVM-based EEG decoding: A within- and between-subjects perspective

Normalization is widely used in electroencephalogram (EEG)-based multivariate pattern classification (MVPC) to reduce magnitude differences across trials and subjects. However, the spatial normalization method as applied to EEG channel-based brain maps has been rarely investigated in EEG-based decoding tasks like event-related potential (ERP) experiments. Meanwhile, the effectiveness of spatial normalization across diverse experimental paradigms remains unclear. This study evaluated the impact of spatial normalization on decoding accuracy using the support vector machine (SVM). The analysis included nine experimental paradigms, with seven binary ERP paradigms, one four-class facial expression paradigm, and one sixteen-class orientation paradigm. Results showed that spatial normalization significantly improved the between-subjects decoding accuracy (Cohen’s d = 1 . 39 , p < 0 . 001 ) but did not enhance the within-subjects decoding accuracy. Additionally, the morphological fidelity of the difference wave was preserved after spatial normalization, as evidenced by the high similarity between the normalized and original ERP difference waves across the seven binary paradigms. We validated our findings across diverse experimental paradigms and demonstrated that spatial normalization effectively enhances between-subjects decoding accuracy using SVM while preserving the temporal consistency of ERP, offering a generalizable preprocessing approach for EEG-based cognitive, clinical, and brain–computer interface (BCI) applications. • The effect of spatial normalization was evaluated on the EEG decoding performance using SVM. • Nine diverse paradigms (seven binary, two multi-category) were examined in the between and within-subjects conditions. • Spatial normalization enhanced between-subjects decoding accuracy but not within-subjects decoding accuracy. • Spatially normalized data preserved the morphological fidelity of the difference waves as evidenced by seven binary paradigms.