肯德尔转移:一种用于估计复杂系统中信息转移的新方法
Xin Wen1,2, Zhenhu Liang1,2, Jing Wang3
1Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China.
Journal of neural engineering
|July 10, 2023
概括
我们介绍了肯德尔转移 (KTE),这是一种新的方法,可以增强神经科学中的因果推理. KTE在小样本尺寸方面表现出色,在分析神经信号的准确性和稳定性方面超过现有技术.
科学领域:
- 神经科学是一个神经科学.
- 信息理论 信息理论
- 动态系统分析 动态系统分析
背景情况:
- 转移 (TE) 对于推断动态系统中的因果关系至关重要,特别是在神经科学中.
- 在神经信号分析中,由于样本大小小,估计TE经常面临局限性.
- 肯德尔转换为有限数据的信息理论测量提供了优势.
研究的目的:
- 在TE估计中引入肯德尔相关性,创建肯德尔TE (KTE).
- 与现有 TE 估计方法相比,评估 KTE 的有效性.
- 用小样本大小和噪音数据来评估KTE的表现.
主要方法:
- 通过将 Kendall 转换与 TE 估计相结合,开发了 Kendall TE (KTE).
- 与自适应分区 (D-V分区) 和符号 TE.相比,KTE.
- 使用模拟 (线性,非线性,神经质量模型) 和真实EEG数据的验证方法.
主要成果:
- 在小样本大小的情况下,KTE在识别合方向方面表现出卓越的性能.
- KTE对合强度和抗噪声的敏感性增加.
- 在EEG中,KTE准确地检测到在波诱导麻醉期间改变的前额-双侧连接.
结论:
- 肯德尔 TE (KTE) 是一个强大而强大的工具,用于估计转移,特别是在有限的数据.
- KTE提供了一种创新的方法来量化信息理论分析的时间序列.
- 该方法在神经科学和其他领域推进因果推理方面具有重大潜力.
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