50小时单人脑磁图数据，助力语音解码模型大规模训练
LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
- 构建超大单人脑磁图语音数据集，支持深度学习训练
- 数据量达同类研究5倍以上，显著提升解码准确率
- 适合脑机接口、神经解码研究者使用
LibriBrain是目前最大规模的单被试脑磁图（MEG）语音解码数据集，包含超过50小时的记录，是次大数据集的5倍、多数数据集的50倍。该数据集涵盖单一受试者聆听自然语境英语录音的高质量MEG信号，内容覆盖几乎全部《福尔摩斯探案集》。数据集配有Python工具库，支持与深度学习框架无缝集成，提供标准数据划分和三类基础解码任务的基线结果：语音检测、音素分类与词汇分类。基线实验表明，增加训练数据可显著提升解码性能，凸显深度纵向数据的价值。通过公开此数据集，旨在推动神经解码方法发展，加速安全高效的临床脑机接口研发。
原文摘要 · Abstract (English)
LibriBrain represents the largest single-subject MEG dataset to date for speech decoding, with over 50 hours of recordings -- 5$\times$ larger than the next comparable dataset and 50$\times$ larger than most. This unprecedented `depth' of within-subject data enables exploration of neural representations at a scale previously unavailable with non-invasive methods. LibriBrain comprises high-quality MEG recordings together with detailed annotations from a single participant listening to naturalistic spoken English, covering nearly the full Sherlock Holmes canon. Designed to support advances in neural decoding, LibriBrain comes with a Python library for streamlined integration with deep learning frameworks, standard data splits for reproducibility, and baseline results for three foundational decoding tasks: speech detection, phoneme classification, and word classification. Baseline experiments demonstrate that increasing training data yields substantial improvements in decoding performance, highlighting the value of scaling up deep, within-subject datasets. By releasing this dataset, we aim to empower the research community to advance speech decoding methodologies and accelerate the development of safe, effective clinical brain-computer interfaces.
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