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arXiv AI

Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS

The paper presents a method for zero‑shot cross‑subject continuous affect regression using synchronized EEG‑fNIRS data. It decomposes affect trajectories into a shared component across subjects and an individual component derived from a label‑free alpha‑band cross‑channel synchrony marker, which rescales the shared trajectory for each test subject. Extensive validation—including leave‑one‑subject‑out correlation, functional‑form comparison, component ablation, and ceiling analysis—shows the approach achieves lower mean absolute errors than EEGNet and ASAC‑Net baselines on unseen subjects.

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arXiv AI
2d ago

Low-Cost Video--Time Priors as a Strong Baseline for EEG--fNIRS Emotion Regression on Familiar Videos

The paper presents a low‑cost baseline for continuous emotion regression that relies on video–time priors when users watch familiar videos. It compares this baseline to a fusion of EEG and fNIRS signals, finding that the video–time prior alone achieves mean absolute errors within 0.05 and 0.32 of the fusion model in internal and external evaluations. Ablation studies show that video identity and within‑video time explain most of the performance, while EEG–fNIRS contributions are smaller and variable across participants and videos.

By Minghao Kong, Jiurun Chen, Ying Gao, Xiangbin Meng, Rongjie Wang
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arXiv Machine Learning
2d ago

ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models

ZeroMAG is a zero‑shot multimodal adapter generation framework that extends frozen EEG foundation models to heterogeneous multimodal recordings using only unlabeled target data. It constructs a configuration‑invariant adapter and generates adapter weights in a latent space learned from source adapters, without target‑side optimization. Across six held‑out target datasets and three EFM backbones, ZeroMAG improves balanced accuracy by 7.22 percentage points over EEG‑only inference and 4.89 points over direct weight regression, approaching supervised multimodal adaptation.

By Yubo Wang, Jingying Ma, Xinliang Zhou, Yangxuan Zhou, Jiquan Wang, Sha Zhao, Yiyuan Yang, Yi Ding, Ziyu Jia, Chenyu Liu, Cuntai Guan
diffusionmultimodal
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arXiv AI
Sep 28

Neural State Prediction: Obstructing Shortcut Learning in EEG Foundation Models

Neural State Prediction (NSP) is a latent‑predictive framework designed to curb shortcut learning in EEG foundation models. By using a target encoder updated with an exponential moving average, identity residualization, and topology‑separated context, NSP constrains both the prediction target and the available context. Trained on 2.2 million EEG segments, NSP outperforms baselines on 14 datasets in the EEG‑FM‑Bench, achieving 63.94 % macro balanced accuracy.

By Kieren Yu, Ziyang Liu, Chang Huang, Jintai Chen, Kaishun Wu
fine-tuning
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arXiv Machine Learning
Jun 2

OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models

arXiv:2606. 00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions.

By Ziling Lu, Zongsheng Li, Xinke Shen, Kexin Lou, Yingyue Xin, Xiaoqi Chen, Shinan Wang, Xiang Chen, Jiahao Fan, Chenyu Huang, Xin Xu, Zhoujie Hou, Chen Wei, Quanying Liu
llmsbenchmarks
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