用代码本结构提升脑电模型，让脑机接口更准更快
Advancing Brainwave Modeling with a Codebook-Based Foundation Model
- 采用代码本机制增强脑电信号表征能力
- 在多个任务中超越原有架构，性能达开源模型水平
- 适合脑机接口与神经信号分析研究者使用
近年来大规模预训练脑电（EEG）模型在脑机接口（BCI）和医疗应用中展现出巨大潜力。然而，现有预训练模型难以充分捕捉神经振荡的丰富信息，这一根本局限源于架构设计不合理，制约了其性能与跨任务泛化能力。本文提出改进版大型脑波基础模型LaBraM++，基于稳健信号处理原理进行架构优化。该模型在多项任务中表现显著提升，优于原生架构，并达到其他开源脑波基础模型的竞争力水平。其优异性能与高效训练能力，彰显其作为未来脑波模型发展基石的潜力。
原文摘要 · Abstract (English)
Recent advances in large-scale pre-trained Electroencephalogram (EEG) models have shown great promise, driving progress in Brain-Computer Interfaces (BCIs) and healthcare applications. However, despite their success, many existing pre-trained models have struggled to fully capture the rich information content of neural oscillations, a limitation that fundamentally constrains their performance and generalizability across diverse BCI tasks. This limitation is frequently rooted in suboptimal architectural design choices which constrain their representational capacity. In this work, we introduce LaBraM++, an enhanced Large Brainwave Foundation Model (LBM) that incorporates principled improvements grounded in robust signal processing foundations. LaBraM++ demonstrates substantial gains across a variety of tasks, consistently outperforming its originally-based architecture and achieving competitive results when compared to other open-source LBMs. Its superior performance and training efficiency highlight its potential as a strong foundation for future advancements in LBMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。