对BCI电机解码电路设计的比较指标和功率权衡
Joe Saad1, Adrian Evans1, Ilan Jaoui2
1Université Grenoble Alpes, CEA, LIST, Grenoble, France.
Frontiers in human neuroscience
|March 27, 2025
概括
低功耗的大脑计算机接口 (BCI) 硬件对于辅助设备至关重要. 这项研究表明,增加频道可以降低每个频道的功耗,同时提高解码脑信号 (如EEG和ECoG) 的信息传输速率.
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
- 计算机工程 计算机工程
背景情况:
- 脑计算机接口 (BCI) 对于康复和辅助技术至关重要,需要植入式和电池驱动设备的低功耗硬件.
- 有效的运动和语音解码需要在脑计算机接口 (BCI) 中高效的信号处理.
研究的目的:
- 审查现有的BCI硬件系统,重点关注电机解码,并分析影响功率和算法性能的因素.
- 提出比较芯片上的解码系统对电脑学 (EEG),电皮质学 (ECoG) 和微电极阵列 (MEA) 信号的能效指标.
主要方法:
- 对BCI现有硬件系统的分析,重点是电机解码.
- 开发和应用指标来比较各种芯片上的解码系统 (EEG,ECoG,MEA) 的能源效率.
- 审查最先进的解码电路的优化,以最大限度地降低功耗.
主要成果:
- 给定分类率需要经验可估计的输入数据速率 (IDR),有助于设计新的BCI系统.
- 每个通道的功耗 (PpC) 和信息传输速率 (ITR) 之间存在一个反直觉的负相关性.
- 增加频道数量可以通过硬件共享来减少PpC,并通过结合更多数据来增加ITR;电力消耗主要由EEG和ECoG的信号处理复杂性所主导.
结论:
- 硬件共享和增加频道数量提供了一种途径,可以同时降低每个频道的功耗,并提高BCI中的信息传输速度.
- 优化信号处理的复杂性是最大限度地减少EEG和ECoG解码电路的电力消耗的关键.
- 这些发现为设计用于现实世界辅助应用的节能BCI系统提供了宝贵的见解.
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