实时即时相位估计使用深度双分支复杂神经网络
IEEE transactions on bio-medical engineering
|December 23, 2025
概括
这项研究引入了一种新的深度学习算法,用于精确的神经振荡相位估计,这对于大脑-计算机接口至关重要. 该方法提高了准确性,并针对边缘设备进行了优化,改善了实时大脑接口应用程序.
科学领域:
- 神经科学是一个神经科学.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 精确估计神经振荡阶段对于大脑接口技术至关重要,如大脑计算机接口 (BCI) 和神经调节.
- 传统的相位估计方法,例如使用离散里埃变换 (DFT) 的希尔伯特变换,由于依赖过去和现在的数据,引入相位滞后.
研究的目的:
- 开发一种深度学习算法,用于精确的神经振荡即时相位估计.
- 为实时应用设计一个适合在资源有限的边缘设备 (如FPGA) 上部署的算法.
主要方法:
- 开发了一种新的深度学习算法,采用双分支结构,灵感来自复杂的波形变换.
- 使用离散的等号变换 (DCT) 层来提取信号组件的潜在表示,生成伪复杂的信号.
- 该算法是为了提高效率而设计的,其目标是部署在具有有限计算能力的便携式边缘设备上.
主要成果:
- 拟议的深度学习模型显示了相位估计精度的显著改善.
- 与端点校正的希尔伯特变换 (ecHT) 方法相比,准确度提高了 40.3%.
- 与传统的深度学习架构相比,显示了9.2%的改进.
结论:
- 开发的深度学习算法为神经信号的瞬时阶段估计提供了更准确和更有效的方法.
- 这项原则证明工作验证了神经调节和其他BCI应用中实时阶段估计的潜力.
- 该算法的适用于边缘设备的适用性为先进的,便携式大脑接口系统开辟了可能性.
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