面向样本高效神经接口的预训练
Pretraining for Sample-Efficient Neural Interfaces
- Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出MAPA自监督预训练方法，通过解剖区域嵌入和相对位置编码，在无需微调下提升脑机接口跨受试者解码性能，显著减少所需标签数据。
AI中文摘要：
脑机接口（BCI）通过解码神经活动来恢复丧失的功能。通常，训练高性能的神经解码器需要从每位新受试者收集大量带标签的数据集。减少带标签数据成本的一种方法是自监督预训练，它从跨受试者积累的无标签记录中学习通用的神经表征。然而，对于颅内脑电图（iEEG）记录，由于受试者之间接触位置和神经解剖结构的差异，自监督学习一直具有挑战性。我们提出了MAPA，一个带有两种空间编码（解剖区域嵌入和相对位置编码）的普通掩码自编码器，这两种编码共同使其能够学习可迁移到未见受试者和各种任务的神经表征。在Neuroprobe基准的所有三种设置（会话内、跨会话和跨受试者）中，MAPA在无需微调的情况下均达到了新的最先进水平。在跨受试者设置中，基于MAPA特征的线性探针仅需约164个带标签试验即可达到未预训练时需3500个试验才能达到的准确率。我们的结果表明，自监督预训练可以扩展到异质的iEEG记录，并减少新受试者准确解码所需的带标签数据。
英文摘要：
Brain-computer interfaces (BCIs) decode neural activity to restore lost function. Typically, training a high-performance neural decoder requires a large labeled dataset to be collected from every new subject. One way to reduce the labeled data cost is self-supervised pretraining, which learns general neural representations from unlabeled recordings that accumulate across subjects. However, for intracranial electroencephalography (iEEG) recordings, self-supervised learning has been challenging due to differences in contact placement and neuroanatomy between subjects. We propose MAPA, an otherwise vanilla masked autoencoder with two spatial encodings, an anatomical region embedding and a relative positional encoding, which together enable it to learn neural representations that transfer to unseen subjects and across various tasks. MAPA sets a new state of the art across all three regimes of the Neuroprobe benchmark without fine-tuning: within-session, cross-session, and cross-subject. In the cross-subject regime, a linear probe on MAPA's features needs only ${\sim}164$ labeled trials to reach the accuracy that takes 3,500 without pretraining. Our results show that self-supervised pretraining can scale across heterogeneous iEEG recordings and reduce the labeled data needed for accurate decoding in new subjects.
