用于脑机接口神经解码泛化的自监督一致性增强解缠学习
Self-Supervised Consistency Enhanced Disentangled Learning for Neural Decoding Generalization in Brain-Machine Interface
- College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
- Nanhu Brain-Computer Interface Institute（南湖脑机接口研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究脑机接口因神经漂移致性能下降问题，提出自监督一致性增强解缠学习框架（SSCDL）。通过设计一致性增强神经解码器及采用互补解缠泛化机制，学习鲁棒表征，实现跨日泛化，提升解码性能，展现长期交互潜力。
AI中文摘要：
脑机接口（BMI）为大脑与外部设备提供直接通信路径，使人类能控制辅助和机器人技术，在康复、人类运动增强和以人类为中心的机器人技术中有潜在应用。然而，由于神经漂移，BMI的性能随时间下降，给长期可行性带来挑战，尤其是侵入性BMI（iBMI）。现有解决方案有两个主要缺点：难以学习鲁棒的神经表征，以及忽视神经漂移因运动参数（如速度、方向和速率）而异。为克服这些限制，我们提出自监督一致性增强解缠学习（SSCDL），这是一个基于两项关键创新构建的神经解码泛化框架。我们首先设计一个名为一致性增强神经解码器（CND）的主干模型，使用带有模拟神经信号扰动的新型师生一致性约束来学习对神经漂移不变的鲁棒表征。然后，我们在互补解缠泛化（CDG）机制下采用三个专用的CND，该机制从神经偏好理论中获得灵感，将运动信号解缠为速度、方向和速率。这种解缠学习使SSCDL能够从不同的神经偏好角度捕获不变的神经表征，显著增强跨日泛化能力。广泛的实验结果表明，SSCDL提供了最先进的解码性能，表现出高鲁棒性和跨日稳定性。这些能力突出了其在以人类为中心的机器人技术和细粒度辅助应用中进行长期交互的强大潜力。
英文摘要：
Brain-Machine Interfaces (BMIs) provide a direct communication pathway between the brain and external devices, enabling humans to control assistive and robotic technologies, with potential applications in rehabilitation, human motor augmentation, and human-centered robotics. However, due to neural drift, the performance of BMIs decreases over time, posing challenges for long-term viability, particularly for invasive BMIs (iBMIs). Existing solutions suffer from two main drawbacks: (i) difficulty in learning robust neural representations, and (ii) neglecting that neural drift varies across motor parameters (e.g., velocity, direction, and speed). To overcome these limitations, we propose Self-Supervised Consistency enhanced Disentangled Learning (SSCDL), a neural decoding generalization framework built on two key innovations. We first design a backbone model named Consistency enhanced Neural Decoder (CND), using a novel teacher-student consistency constraint with simulated neural signal perturbations to learn robust representations invariant to neural drift. Then, we employ three dedicated CNDs under the Complementary-Disentangled Generalization (CDG) mechanism, which disentangles motor signals into velocity, direction, and speed with inspiration from neural preference theory. This disentangled learning enables SSCDL to capture invariant neural representations from diverse neural preference perspectives, significantly enhancing cross-day generalization. Extensive experimental results show that SSCDL delivers state-of-the-art decoding performance, exhibiting high robustness and cross-day stability. These capabilities underscore its strong potential for long-term interaction in human-centric robotic and fine-grained assistive applications.
