LibriBrain100：用于大规模神经语音解码的一百小时宽深脑磁图数据集
LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale
浏览论文内容
中文总结 AI 辅助
本研究推出含100小时脑磁图数据的LibriBrain100数据集，通过深度受试者内数据与多受试者数据结合，验证其可提升单词分类解码性能，助力无创脑机接口发展。
中文摘要 AI 辅助
我们推出LibriBrain100，这是一个专为可复现、标准化评估设计的大规模脑磁图（MEG）语音解码数据集。LibriBrain100的规模是原始LibriBrain发布版的两倍多，包含超过100小时的高质量脑磁图数据，这些数据是在受试者聆听自然连续语音时采集的。其中单个受试者的数据约为80小时，LibriBrain100创下了深度、受试者内神经数据的新纪录——比下一个可比数据集多8倍，比其他数据集多约80倍。为了展示这种深度优先设计的价值，我们在单词分类基准上进行了评估，该基准是向无创脑-文本解码这一公开挑战迈进的日益成熟的垫脚石。使用现有解码模型，我们取得了最先进的性能，验证了记录的质量和大规模受试者内数据的价值。由于每个用户采集80小时数据在实际应用中不切实际，我们还从32名受试者中每人额外采集了约40分钟的数据。使用相同的单词分类基准，我们证明了广泛的多受试者数据的价值：对预训练模型进行监督微调可大幅弥补每个受试者数据有限的问题。我们提供标准的训练、验证和测试划分，所有划分都可通过开源Python库复现，该库支持轻松下载、可选预处理以及为常见深度学习框架加载数据。此外，该数据集和评估基础设施将与一个开放机器学习竞赛一同发布，该竞赛带有用于标准化基准测试的公开排行榜。最终，我们希望LibriBrain100能加速实用无创脑机接口的发展，这种接口能够为患有严重瘫痪的人群恢复交流能力。
英文摘要
We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. LibriBrain100 more than doubles the size of the original LibriBrain release, resulting in over 100 hours of high-quality MEG acquired while subjects listened to naturalistic continuous speech. With $\sim$80 hours from a single subject, LibriBrain100 sets a new record for deep, within-subject neural data (8$\times$ more than the next comparable dataset and roughly 80$\times$ more than other datasets). To demonstrate the payoff of this depth-first design, we evaluate on a word-classification benchmark---an increasingly well-established stepping stone towards the open challenge of noninvasive brain-to-text decoding. Using an existing decoding model, we achieve state-of-the-art performance---validating both the quality of the recordings and the value of within-subject data at scale. Because collecting 80 hours of data per user is impractical for real-world applications, we also collected $\sim$40 minutes of additional data from each of 32 subjects. Using the same word-classification benchmark, we demonstrate the value of broad multi-subject data: supervised finetuning of a pre-trained model can substantially compensate for limited per-subject data. We provide standard train, validation, and test splits, all reproducible through an open-sourced Python library that supports easy downloading, optional preprocessing, and data loading for common deep learning frameworks. In addition, the dataset and evaluation infrastructure are being released alongside an open machine-learning competition with a public leaderboard for standardised benchmarking. Ultimately, our hope is that LibriBrain100 will accelerate progress towards practical non-invasive brain-computer interfaces, capable of restoring communication to people living with severe paralysis.
发表机构
- University of Oxford（牛津大学）
- FMRIB
- OHBA
机构由 AI 辅助整理，请以论文原文为准。
