TGSD combines a Hierarchical Spatial Prior Encoder with conditional state-space diffusion to achieve EEG spatial super-resolution, outperforming baselines on reconstruction fidelity and classification on SEED and PhysioNet datasets.
Linguistics and Human Brain: A Perspective of Computational Neuroscience
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abstract
Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistics and the empirical neural data of neuroscience. Serving as an interdisciplinary cornerstone, computational neuroscience formalizes the hierarchical and dynamic structures of language into testable neural models through modeling, simulation, and data analysis. This enables a computational dialogue between linguistic hypotheses and neural mechanisms. Recent advances in deep learning, particularly large language models (LLMs), have powerfully advanced this pursuit. Their high-dimensional representational spaces provide a novel scale for exploring the neural basis of linguistic processing, while the "model-brain alignment" framework offers a methodology to evaluate the biological plausibility of language-related theories.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SUP-MCRL reports 66.0%/91.9% intra-subject and 24.0%/52.9% LOSO zero-shot top-1/top-5 accuracy on THINGS-EEG by combining semantic visual encoding, multi-scale EEG enhancement, and EMA-updated pseudo-feature augmentation.
citing papers explorer
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TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution
TGSD combines a Hierarchical Spatial Prior Encoder with conditional state-space diffusion to achieve EEG spatial super-resolution, outperforming baselines on reconstruction fidelity and classification on SEED and PhysioNet datasets.
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SUP-MCRL: Subject-aware Unified Pseudo-feature Coded Multimodal Contrastive Representation Learning for EEG Visual Decoding
SUP-MCRL reports 66.0%/91.9% intra-subject and 24.0%/52.9% LOSO zero-shot top-1/top-5 accuracy on THINGS-EEG by combining semantic visual encoding, multi-scale EEG enhancement, and EMA-updated pseudo-feature augmentation.