用任务条件对齐提升有限数据下的神经信号解码效果
Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment
- 通过任务感知的潜在空间对齐，迁移源会话知识到目标会话
- 在猴子运动/眼动数据集上，解码准确率提升最高达0.386
- 适合神经假肢、脑机接口中数据稀缺场景使用
当仅能获取少量目标会话数据时，训练高性能神经解码器面临挑战。为此，我们提出任务条件潜在对齐框架（TCLA），用于跨会话神经解码。基于自编码器架构，TCLA首先从数据丰富的源会话中学习低维神经表征；针对数据有限的目标会话，再以任务条件方式将目标潜空间对齐至源会话，从而实现神经表征的有效迁移，支持目标会话的解码器训练。我们在猕猴运动与眼动中心出发数据集上评估TCLA。相比仅在目标会话数据上训练的基线方法，TCLA在多个数据集与解码设置下均一致提升性能，尤其在运动数据集中对y方向速度解码的决定系数最高提升0.386。结果表明，TCLA为在数据受限条件下实现源会话到目标会话的知识迁移提供了有效策略。
原文摘要 · Abstract (English)
Training a high-performing neural decoder can be difficult when only limited data are available from a recording session. To address this challenge, we propose a Task-Conditioned Latent Alignment framework (TCLA) for cross-session neural decoding with limited target-session data. Building upon an autoencoder architecture, TCLA first learns a low-dimensional neural representation from a source session with sufficient data. For target sessions with limited data, TCLA then aligns the target latent representations to the source session in a task-conditioned manner, enabling effective transfer of learned neural representations to support decoder training in the target session. We evaluate TCLA on the macaque motor and oculomotor center-out datasets. Compared to baseline methods trained solely on target-session data, TCLA consistently improves decoding performance across datasets and decoding settings, with gains in the coefficient of determination of up to 0.386 for y coordinate velocity decoding in a motor dataset. These results suggest that TCLA provides an effective strategy for transferring knowledge from source to target sessions, improving neural decoding performance under conditions with limited target-session data.
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