CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
Yuchen Zhou, Jiamin Wu, Zichen Ren, Zhouheng Yao, Weiheng Lu, Kunyu Peng, Qihao Zheng, Chunfeng Song, Wanli Ouyang, Chao Gou
摘要
Understanding and decoding human brain activity from electroencephalography (EEG) signals is a fundamental problem in neuroscience and artificial intelligence, with applications ranging from cognition and emotion recognition to clinical diagnosis and brain-computer interfaces. While recent EEG foundation models have made progress in generalized brain decoding by leveraging unified architectures and large-scale pretraining, they inherit a scale-agnostic dense modeling paradigm from NLP and vision. This design overlooks an intrinsic property of neural activity-cross-scale spatiotemporal structure. Different EEG task patterns span a broad range of temporal and spatial scales, from brief neural activations to slow-varying rhythms, and from localized cortical activations to large-scale distributed interactions. Ignoring this diversity may lead to suboptimal representations and weakened generalization ability. To address these limitations, we propose CSBrain, a Cross-scale Spatiotemporal Brain foundation model for generalized EEG decoding. CSBrain introduces two key components: (i) Cross-scale Spatiotemporal Tokenization (CST), which aggregates multi-scale features within localized temporal windows and anatomical brain regions into compact scale-aware token representations; and (ii) Structured Sparse attention (SSA), which models cross-window and cross-region dependencies for diverse decoding tasks, further enriching scale diversities while eliminating the spurious dependencies. CST and SSA are alternately stacked to progressively integrate cross-scale spatiotemporal dependencies. Extensive experiments across 11 representative EEG tasks and 16 datasets demonstrate that CSBrain consistently outperforms both task-specific models and strong foundation baselines. These results establish cross-scale modeling as a key inductive bias for generalized EEG decoding and highlight CSBrain as a robust backbone for future brain-AI research. The code and model are available at https://github.com/yuchen2199/CSBrain.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper11
- CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation ModelJingying Ma, Feng Wu, Qika Lin, Yucheng Xing 等ICLR 2026 · 被引用 25 次
- Uni-NTFM: A Unified Foundation Model for EEG Signal Representation LearningZhisheng Chen, Yingwei Zhang, Qizhen Lan, Tianyu Liu 等ICLR 2026 · 被引用 16 次
- ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG ModelsChenyu Liu, Yuqiu Deng, Tianyu Liu, Jinan Zhou 等ICLR 2026 · 被引用 12 次
- SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation LearningWeijian Mai, Jiamin Wu, Yu Zhu, Zhouheng Yao 等NeurIPS 2025 · 被引用 11 次
- NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural ActivityWeijian Mai, Mu Nan, Yu Zhu, Jiahang Cao 等CVPR 2026
它引用的顶会 Paper21
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- An Image is Worth 16x16 Words: Transformers for Image Recognition at ScaleAlexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn 等ICLR 2021 · 被引用 21,477 次
- Visual Instruction TuningHaotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae LeeNeurIPS 2023 · 被引用 11,349 次
- CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped WindowsXiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang 等CVPR 2022 · 被引用 1,207 次
相关 Paper
- CBraMod: A Criss-Cross Brain Foundation Model for EEG DecodingJiquan Wang, Sha Zhao, Zhiling Luo, Yangxuan Zhou 等ICLR 2025
- BaRISTA: Brain Scale Informed Spatiotemporal Representation of Human Intracranial Neural ActivityLucine L. Oganesian, Saba Hashemi, Maryam M. ShanechiNeurIPS 2025 · 被引用 7 次
- MindMix: A Multimodal Foundation Model for Auditory Perception Decoding via Deep Neural-Acoustic AlignmentRUI LIU, Zhige Chen, Pengshu, Wenlong You 等ICLR 2026
- NeurIPT: Foundation Model for Neural InterfacesZitao Fang, Chenxuan Li, Hongting Zhou, Shuyang Yu 等NeurIPS 2025 · 被引用 16 次
- REVE: A Foundation Model for EEG - Adapting to Any Setup with Large-Scale Pretraining on 25, 000 SubjectsYassine El Ouahidi, Jonathan Lys, Philipp Thölke, Nicolas Farrugia 等NeurIPS 2025 · 被引用 106 次
