← Back to all news
arXiv Computer Vision By Haoliang Gong, Qingshan She, Jiale Xu, Yunyuan Gao, Xugang Xi

MSCGC-KAN: Multi-scale Causal Graph Convolution and KAN-inspired Analytic-basis Mapping for EEG Emotion Recognition

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Machine Learning
Aug 31

One Model for All: Universal Pre-training for EEG based Emotion Recognition across Heterogeneous Datasets and Paradigms

The paper introduces ‘One Model for All’, a universal pre‑training framework that tackles EEG‑based emotion recognition across diverse datasets and paradigms. It decouples learning into a univariate self‑supervised contrastive pre‑training stage using a Unified Channel Schema, followed by a multivariate fine‑tuning stage that employs an Adaptive Resampling Transformer and a Graph Attention Network to model spatio‑temporal dependencies. Experiments demonstrate state‑of‑the‑art performance on within‑subject benchmarks (SEED 99.27%, DEAP 93.69%, DREAMER 93.93%) and superior cross‑dataset transfer, with ablation studies highlighting the critical role of the GAT module.

By Xiang Li, You Li, Yazhou Zhang
llmsfine-tuningbenchmarks
More like this →
arXiv Machine Learning
Sep 22

Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures

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
llmsbenchmarkssafety
More like this →