arXiv:2607.27655v2 Announce Type: replace
Abstract: Checkpoint selection can improve an electroencephalography (EEG) emotion-recognition score without improving performance on other trials. We compar...
By Hanting Suo, Hongxun Wang, Yuwen Li
The paper introduces MSCGC-KAN, a new EEG emotion recognition approach that builds on a pre‑trained CBraMod backbone. It incorporates a structured task head featuring multi‑scale causal graph convolution and Kolmogorov–Arnold feature mapping to better capture multi‑scale emotional dynamics, inter‑channel connectivity, and nonlinear discriminative patterns. Experiments on FACED and SEED‑VII show significant performance gains over a linear baseline, achieving balanced accuracies of 60.66% and 33.27% respectively.
By Haoliang Gong, Qingshan She, Jiale Xu, Yunyuan Gao, Xugang Xi
The paper introduces EmoDiPyraTrans, a development-regularized differential graph Transformer designed to decode emotions from EEG signals across unseen individuals and populations while maintaining neural interpretability. Evaluations on five datasets (SEED, FACED, MAHNOB-HCI, DEAP, DREAMER) show high cross‑subject accuracies, with the model outperforming others in accuracy and positive‑class F1. Additional experiments demonstrate its ability to distinguish healthy from depressed participants and reveal spatial‑spectral neural signatures across frontal, temporal, central, and parietal regions with an alpha‑centered frequency preference.
By Dongyi He, Bin Jiang, Xiangkai Wang, Yun Zhao, Hongjie Yan, Wai Ting Siok, Nizhuan Wang
arXiv:2606. 06647v1 Announce Type: new Abstract: Objective.
By Jun-You Lin, Ying Choon Wu, Tzyy-Ping Jung
arXiv:2607. 24519v3 Announce Type: replace-cross Abstract: EEG foundation-model gains may depend on cohort, montage, or probe design.
By Marzieh Zare
arXiv:2508. 17742v3 Announce Type: replace-cross Abstract: Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific progress.
By Wei Xiong, Jiangtong Li, Jie Li, Kun Zhu, Changjun Jiang