降低iBCI中高精度解码的功率要求
Brianna M Karpowicz1, Bareesh Bhaduri1, Samuel R Nason-Tomaszewski1
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States of America.
Journal of neural engineering
|October 18, 2024
概括
这项研究引入了使用局部场潜力 (LFP) 来重建神经发射速率的脑计算机接口的新方法. 这种方法可以提高解码精度,并减少皮质内脑计算机接口 (iBCI) 的功耗.
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:
- 皮层内脑计算机接口 (iBCI) 通常使用神经尖端来解码,需要高采样率的数据.
- 局部场势 (LFP) 提供了一个替代的,带宽较低的信号,但在历史上显示的解码性能低于尖峰.
- 现有的基于LFP的解码方法无法与基于尖峰的解码用于实时控制的准确性相匹配.
研究的目的:
- 开发和验证一种新的策略,以提高iBCI中的基于LFP的解码性能.
- 使用神经动态模型从LFP重建神经发射率.
- 为了从LFP中实现高精度解码,接近基于尖峰的性能,同时降低系统要求.
主要方法:
- 训练有素的神经动态模型使用LFP重建底层的神经发射率.
- 测试了基于LFP的重建和解码策略,用于达任务和人类语音尝试数据.
- 基于LFP的动态模型与直接尖峰解码和LFP单独解码的解码性能比较.
主要成果:
- 基于LFP的神经动力学模型实现了与基于尖峰模型相比的火速重建精度.
- 使用基于LFP的动态模型的解码性能超过了单独使用LFP的性能,并且接近基于尖峰模型的性能.
- 在大多数应用中,基于LFP的动态模型在精度上超过了直接尖峰解码.
结论:
- 提出的基于LFP的动态模型显著提高了iBCI的解码性能.
- 这种方法允许使用更低的带宽和采样率实现高精度的神经解码,从而降低了iBCI电源需求.
- 研究结果表明,在不损害控制精度的情况下,可以实现更高效和实用的iBCI系统.
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