Electroencephalography (EEG) captures neural activity across multiple temporal and spectral scales, yielding signals that are rich but complex for representation learning. Recently, EEG foundation models trained to predict masked signal-tokens have shown promise for learning generalizable representations. However, their performance is hindered by their signal tokenization modules. Existing neural tokenizers fail to preserve high-frequency dynamics, limiting their ability to reconstruct EEG signals with high fidelity. We introduce NeuroRVQ, a scalable Large Brainwave Model (LBM) centered on a codebook-based tokenizer. Our tokenizer integrates: (i) multi-scale feature extraction modules that capture the full frequency neural spectrum; (ii) hierarchical residual vector quantization (RVQ) codebooks for high-resolution encoding; and, (iii) an EEG signal phase- and amplitude-aware loss function for efficient training. This design enables efficient EEG compression while supporting accurate reconstruction across all frequency bands, leading to robust generative masked modeling. Our empirical results demonstrate that NeuroRVQ achieves lower reconstruction error and outperforms existing LBMs on a variety of downstream tasks. More broadly, NeuroRVQ tokenizer establishes a strong prior for codebook-based general-purpose brainwave models, enabling advances in neural decoding, generative modeling and multimodal biosignal integration.

翻译：脑电图（EEG）捕捉了跨多时间与频谱尺度的神经活动，产生的信号虽丰富但难以用于表示学习。近年来，通过训练预测掩码信号标记的EEG基础模型，在学习可泛化表示方面展现出潜力。然而，其性能受限于信号标记化模块。现有神经标记化方法未能保留高频动态特性，限制了高保真重建EEG信号的能力。本文提出NeuroRVQ，一种以基于码本的标记器为核心的可扩展大型脑波模型（LBM）。我们的标记器整合了：（i）捕获全频段神经频谱的多尺度特征提取模块；（ii）用于高分辨率编码的层次化残差向量量化（RVQ）码本；（iii）面向EEG信号相位与振幅感知的损失函数以实现高效训练。该设计在支持全频段精确重建的同时实现高效EEG压缩，从而构建鲁棒的生成式掩码建模。实验结果表明，NeuroRVQ在多种下游任务中实现了更低的重建误差，并优于现有LBM方法。更广泛而言，NeuroRVQ标记器为基于码本的通用脑波模型建立了强先验，推动了神经解码、生成建模及多模态生物信号集成领域的进展。