AI 中文总结
本文提出BLPM模型，通过CELP编码器与MQSD模块对齐EEG语义，解决EEG基础模型预训练的关键挑战，在多基准任务中实现良好泛化。
AI 中文摘要
近期EEG基础模型的进展已证明，大规模预训练具备实现跨被试、记录环境及数据集的可泛化神经解码的潜力。然而，主流预训练范式面临关键挑战：掩码自编码倾向于优先重构低层级信号而非任务相关语义，而自回归建模则造成连续神经动态与离散令牌空间间的不匹配。为应对这些挑战，需新策略有效对齐连续EEG表示与自然语言语义，并实现其与大语言模型的整合。据此，本文提出脑隐式预测模型（Brain Latent Predictive Model, BLPM），一款EEG-语言基础模型，将异构EEG解码任务重新表述为连续语义嵌入预测问题。BLPM引入连续EEG隐式预测（Continuous EEG Latent Predictive, CELP）编码器，通过隐式目标预测学习可迁移表示；在此基础上，多查询语义分解（Multi-Query Semantic Decomposition, MQSD）模块提取任务相关信息，并依据语义关系在共享隐空间内对齐连续EEG表示与文本语义。跨多个基准的实验表明，该模型在多样化任务中展现出一致的泛化性能，确立连续隐式语义预测为EEG-语言基础模型的有效范式。
英文摘要
Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments, and datasets. However, dominant pretraining paradigms face key challenges: masked autoencoding tends to prioritize low-level signal reconstruction over task-relevant semantics, while autoregressive modeling creates a mismatch between continuous neural dynamics and discrete token spaces. To address these challenges, new strategies are needed to effectively align continuous EEG representations with natural-language semantics and enable their integration with large language models. Accordingly, we propose Brain Latent Predictive Model (BLPM), an EEG-language foundation model that reformulates heterogeneous EEG decoding tasks as a continuous semantic embedding prediction problem. BLPM introduces a Continuous EEG Latent Predictive (CELP) encoder that learns transferable representations through latent target prediction. Building on these representations, a Multi-Query Semantic Decomposition (MQSD) module extracts task-relevant information and aligns continuous EEG representations with textual semantics within a shared latent space according to their semantic relationships. Experiments across multiple benchmarks demonstrate consistent generalization performance across diverse tasks, establishing continuous latent semantic prediction as an effective paradigm for EEG-language foundation models.
Comments19 pages, 3 figures; supplementary material included
