用自发脑电数据预训练自编码器，提升感知解码准确率
Masked Autoencoders Learn Perception-Relevant Representations from Resting State Neural Data
- 用14.6小时自发神经活动预训练掩码自编码器
- 感知解码准确率达84.1%（普通任务）和64.0%（阈值任务）
- 适合神经假肢、无监督脑机接口研究者
临床神经假体面临数据瓶颈：有标签的感知试验稀缺，而数小时的自发神经活动未被充分利用。本文测试自监督学习能否利用这些无标签数据提升感知解码性能。我们在一名盲人受试者V1区的14.6小时多单元自发活动中预训练掩码自编码器。模型在无监督条件下捕捉到可解释的脑结构：V1的空间组织与感知状态分离均从潜在表示中自然浮现。通过线性探测（冻结潜变量上的逻辑回归）评估性能，在刺激数据上，一般心理测验任务解码准确率达84.1%，更难的阈值任务达64.0%。结果表明，自发皮层活动并非噪声，而是蕴含丰富且任务相关的结构。对这类数据进行无监督预训练，是提升神经解码的有前景策略。
原文摘要 · Abstract (English)
Clinical neuroprosthetics face a data bottleneck: labeled perception trials are scarce while hours of spontaneous neural activity are largely underutilized. Here, we test whether self-supervised learning can use these unlabeled datasets to improve perception decoding. We pretrained a masked autoencoder on 14.6 hours of spontaneous multiunit activity from an intracortical array in a blind participant's V1. The model captured interpretable brain structure without supervision: V1's spatial organization and perceptual state separation both emerged purely from its latent representations. To test these features, we used linear probing (logistic regression on the frozen latents) to measure performance on the data with stimulation. Perception decoding accuracy reached 84.1% on a general psychometric task. On the more difficult threshold-level task, accuracy reached 64.0%. This work shows that spontaneous cortical activity is not noise; it contains rich, task-relevant structure. Unsupervised pretraining on this data is a promising strategy to improve neural decoding.
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