通过阶段感知表示学习实现EEG-to-gait解码
Xi Fu1, Weibang Jiang2, Rui Liu1
1College of Computing and Data Science, Nanyang Technological University, 639798, Singapore.
概括
NeuroDyGait使用一种新的两阶段框架从EEG信号中解读下肢运动. 这种脑-计算机接口方法改善了对实时应用程序的动作意图识别和控制.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 从脑电图 (EEG) 信号中精确解码下肢运动对于大脑与计算机接口 (BCI) 的发展至关重要.
- 现有的方法在因果,相一致的预测和处理运动意图识别中的跨主体变异性方面面临挑战.
研究的目的:
- 推出NeuroDyGait,这是一个两阶段,阶段意识的EEG-to-gait解码框架.
- 显式建模时间连续性和域关系,以改进基于EEG的运动解码.
- 为了解决跨主题的变化,并确保BCI应用程序的实时推断.
主要方法:
- 第一个阶段使用相对对比学习与交叉注意力度量来学习语义上对齐的EEG运动嵌入.
- 第二阶段使用会话特定头的动态融合来实现域关系意识解码.
- 该框架在两个基准数据集 (GED和FMD) 上进行了评估.
主要成果:
- 与现有的基线模型相比,NeuroDyGait表现出了显著的性能增长,包括最近的2025模型 (EEG2GAIT).
- 该框架展示了对未见的主题的概括能力.
- 推断延迟在每个窗口保持在5毫秒以下,满足实时BCI要求.
结论:
- NeuroDyGait提供了一种有效的解决方案,用于精确的基于EEG的下肢运动解码.
- 通过可视化技术揭示了步态阶段的可解释的神经相关性.
- 未来的工作将集中在康复应用和多式联通集成,以加强BCI系统.
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