降解意识的神经赋值:在脑机界面中提升解码稳定性
Yun-Ting Kuo1, Han-Lin Wang1, Bo-Wei Chen1
1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei 112304, Taiwan.
APL bioengineering
|April 18, 2025
概括
一种新的可信度加权贝叶斯线性回归 (CW-BLR) 方法有效地归因于大脑机器接口中退化的神经信号. 这种方法可以提高对长期神经记录的解码精度和系统稳定性.
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
- 信号处理 信号处理
背景情况:
- 神经信号退化会影响大脑机器接口 (BMI) 的性能.
- 植入式BMI (iBMIs) 需要强大的信号归算,以获得持续的解码精度.
- 波动的信号质量挑战了长期的IBMI应用.
研究的目的:
- 引入和评估一种可信度加权贝叶斯线性回归 (CW-BLR) 方法,用于赋值退化的神经信号.
- 为了提高iBMI应用中神经解码的稳定性和一致性.
- 为了比较CW-BLR性能与传统的归算技术.
主要方法:
- 开发了一种可信度加权贝叶斯线性回归 (CW-BLR) 方法用于神经信号归算.
- 将CW-BLR与平均归算 (Mean-imp) 和基于高斯混合模型的预期最大化 (GMM-EM) 进行比较.
- 使用核切片逆回归 (kSIR) 解码器来评估27天内从Wistar老鼠的归算数据上的解码性能.
主要成果:
- 与Mean-imp和GMM-EM相比,CW-BLR显著提高了解码精度.
- 该CW-BLR方法有效地保留了神经信号的时间和空间依赖性.
- kSIR解码器与CW-BLR的性能始终更高,特别是在信号降解期间.
结论:
- 在iBMI应用程序中,CW-BLR提供了一个强大的框架,用于赋值退化的神经信号.
- 这种方法提高了解码精度和系统稳定性,在长时间的记录中,信号质量波动.
- 通过利用基于信任的质量指标来实现高级神经解码,CW-BLR的表现优于传统方法.
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