arXiv Machine Learning

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.

arXiv Computer Vision

Aug 31

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

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

fine-tuning

arXiv Machine Learning

Jul 31

Evaluation Protocols and Cross-Subject Generalization in EEG Emotion Recognition

arXiv:2607. 27655v1 Announce Type: new Abstract: Reported accuracy in electroencephalography (EEG) emotion recognition depends on the complete evaluation procedure, not only the classifier.

By Hanting Suo, Yuwen Li

safety

arXiv Machine Learning

Sep 15

Checkpoint Selection and Evaluation in EEG Emotion Recognition

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

arXiv AI

Jul 3

SA-HGNN: Sample-Adaptive Hyperbolic Graph Neural Network for EEG-Based Depression Recognition

arXiv:2607. 02063v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have been widely used to capture spatial functional connectivity patterns to improve electroencephalography (EEG)-based depression recognition performance.

By Yang Li, Pan Hu, Yan Zhang, Wenfan Yang, Tao Wu, Lianbo Guo

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

llms fine-tuning benchmarks

arXiv AI

Jun 2

Dive into Waves: Morlet Spectral Transformer for Cross-Subject Emotion Decoding from EEG

arXiv:2606. 00884v1 Announce Type: cross Abstract: We study cross-subject emotion recognition from EEG, a practically important yet challenging problem in brain-computer interfaces.

By Jiaxin Qing, Lexin Li

llms nlp

arXiv AI

Jun 16

EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models

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

fine-tuning benchmarks safety

arXiv Machine Learning

Sep 7

Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal

Brain4FMs is a unified benchmark for evaluating brain foundation models (BFMs) on both scalp EEG and intracranial EEG (iEEG). It incorporates 17 representative models and 21 public datasets spanning clinical diagnosis, sleep staging, communication, and affective computing, and offers dataset-aware preprocessing, cross‑subject evaluation, and standardized downstream workflows. The benchmark highlights that no single BFM consistently outperforms others across all tasks, modalities, and adaptation protocols, prompting further exploratory analyses of model‑specific spatial, spectral, and discrete representations.

By Fanqi Shen, Enhong Yang, Jiahe Li, Junru Hong, Xiaoran Pan, Zhizhang Yuan, Meng Li, Yang Yang

benchmarks

arXiv Machine Learning

Aug 18

M-LINKX: Multiview Graph Learning for Brain Cognitive Disease Detection

arXiv:2608. 14847v1 Announce Type: new Abstract: Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases.

By An Phan, Yufei Jin, Xingquan Zhu

arXiv Machine Learning

Aug 24

Interpretable Information-Decomposed Brain Graph Learning for fMRI-based Disease Diagnosis

arXiv:2608.20380v1 Announce Type: cross Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided di...

By Dengyi Zhao, Zhiheng Zhou, Zihan Wang, Guiying Yan, Xingqin Qi

arXiv Machine Learning

Jul 7

A Granularity-Aware EEG Feature Framework for Psychopathology Dimension Prediction

arXiv:2607. 02670v1 Announce Type: new Abstract: Electroencephalography (EEG) offers a noninvasive approach for examining neurophysiological correlates of dimensional psychopathology, yet systematic evidence across EEG paradigms and feature granularities remains limited.

By Haofan Cheng, Jingjing Hu, Jingrong Pei, Shuaiqi Fu, Meilun Shen, Shuai Fang, Meng Wang, Dan Guo, Jie Zhang

arXiv AI

Aug 24

Neuro-Geospatial Modelling of EEG Affective States Using Literature-Informed Environmental Context

The study explores whether environmental data can enhance EEG-based affective-state classification by treating such data as literature-informed priors. Using a dual‑tower model that fuses EEG-Conformer representations with a graph‑based environmental encoder, the authors achieve 76.2% accuracy versus 67.4% for EEG alone on a dataset from Astana. Experiments with controls, dose‑response reversal, and domain‑shift show that the improvement is not solely due to environmental information, and replacing Astana’s environmental distribution with Singapore’s reduces accuracy to 72.8%.

By Utsav Poudel, Jagannath Aryal, Subramaniyaswamy Vairavasundaram

multimodal benchmarks

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