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CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
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CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
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Understanding and decoding brain activity from electroencephalography (EEG) signals is a fundamental challenge in neuroscience and AI, with applications in cognition, emotion recognition, diagnosis, and brain-computer interfaces. While recent EEG foundation models advance generalized decoding via unified architectures and large-scale pretraining, they adopt a scale-agnostic dense modeling paradigm inherited from NLP and vision. This design neglects a core property of neural activity: cross-scale spatiotemporal structure. EEG task patterns span a wide range of temporal and spatial scales, from short bursts to slow rhythms, and from localized cortical responses to distributed interactions. Ignoring this diversity leads to suboptimal representations and weak generalization. We propose CSBrain, a Cross-scale Spatiotemporal Brain foundation model for generalized EEG decoding. CSBrain introduces: (i) Cross-scale Spatiotemporal Tokenization (CST), which aggregates multi-scale features from localized temporal windows and anatomical brain regions into compact scale-aware tokens; and (ii) Structured Sparse Attention (SSA), which captures cross-window and cross-region dependencies, enhancing scale diversity while removing spurious correlations. CST and SSA are alternately stacked to progressively integrate multi-scale dependencies. Experiments on 11 EEG tasks across 16 datasets show that CSBrain consistently outperforms task-specific and foundation model baselines. These results establish cross-scale modeling as a key inductive bias and position CSBrain as a robust backbone for future brain-AI research.
Forward citations
Cited by 9 Pith papers
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PRiSE-EEG is a prior-guided EEG foundation model that allocates shared and specialized experts across depth using CKA-derived sigmoid mappings and reports strong cross-paradigm results on 12 benchmarks.
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Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG
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SPOTR is a single-token reconstruction self-supervised pretraining method for EEG, iEEG, ECG, and PPG that reports AUC gains of 4.64-21.71% over baselines under linear probing while cutting latency and memory.
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MSCGC-KAN: Multi-scale Causal Graph Convolution and Kolmogorov-Arnold Feature Mapping for EEG Emotion Recognition
MSCGC-KAN adds multi-scale causal graph convolution and Kolmogorov-Arnold feature mapping as a structured task head on a pre-trained CBraMod backbone, reporting balanced accuracy gains of 5.91 and 2.03 points on FACED...
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Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review
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