用2.5分钟脑电上下文预训练，大幅降低脑机文本接口的数据需求。
MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training
- 采用2.5分钟长上下文预训练，远超以往几秒级别
- 仅用1小时数据即达50小时监督学习性能
- 适合资源受限的临床脑机接口场景
临床脑-文本接口面向无法提供大量训练记录的瘫痪患者。预训练通过跨被试学习统计先验来提升数据效率，但这些先验高度依赖上下文。自然语言可能持续数分钟展开，而现有方法仅使用几秒上下文进行预训练。为此，我们提出MEG-XL，每样本使用2.5分钟的MEG上下文（相当于191k token），比以往工作长5-300倍。在脑数据词解码任务上微调时，MEG-XL仅需少量数据（如1小时）即可达到50小时监督训练的性能，优于现有脑基础模型。结果表明，更长上下文预训练能学习更具泛化性的表示，更好利用长期神经上下文。代码、模型权重及说明已开源。
原文摘要 · Abstract (English)
Clinical brain-to-text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre-training improves data-efficient generalisation by learning statistical priors across subjects, but these priors critically depend on context. While natural speech might unfold gradually over minutes, most methods pre-train with only a few seconds of context. Thus, we propose MEG-XL, a model pre-trained with 2.5 minutes of MEG context per sample, 5-300x longer than prior work, and equivalent to 191k tokens, capturing extended neural context. Fine-tuning on the task of word decoding from brain data, MEG-XL matches supervised performance with a fraction of the data (e.g. 1hr vs 50hrs) and outperforms brain foundation models. We find that models pre-trained with longer contexts learn representations that transfer better to word decoding. Our results indicate that long-context pre-training helps exploit extended neural context that other methods unnecessarily discard. Code, model weights, and instructions are available at https://github.com/neural-processing-lab/MEG-XL .
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