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在神经形态框架中解码大脑信号,用于个性化自适应控制人类假肢

Georgi Rusev¹, Svetlozar Yordanov¹, Simona Nedelcheva¹

¹Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria.

Biomimetics (Basel, Switzerland)

|March 26, 2025

概括

这项研究探讨了脑机接口 (BMI) 的神经形态技术,提出了一种使用3D-SNN和ESN解码电皮层图形 (ECoG) 信号以控制假肢的新框架. 初步结果对适应性,低功耗的BMI系统有希望.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
生物医学工程生物医学工程

背景情况:

当前的大脑机器接口 (BMI) 面临着尺寸,功耗和适应性方面的挑战.
神经形态技术为这些局限性提供了潜在的解决方案.

研究的目的:

研究使用神经形态技术来改进BMI系统的可行性.
提出和评估一种新的神经形态框架,通过电皮层图形 (ECoG) 信号解码来控制假肢.

主要方法:

开发一种新的神经形态框架,集成一个用于特征提取的3D尖峰定时神经网络 (3D-SNN).
实施可在线训练的回声状态网络 (ESN) 进行运动控制解码 (MCD).
在Python中使用NEST模拟器开发的软件系统,能够在实时 (监督/无监督) 持续适应.

主要成果:

拟议的框架在四肢患者ECoG数据上进行了测试.
最初的模拟结果令人鼓舞,证明了该方法的潜力.
通过超参数调整需要进一步改进.

结论:

神经形态技术显示出开发更小,更少耗电,更适应性BMI系统的前景.
拟议的3D-SNN和ESN框架是基于ECoG的假肢控制的可行方法.
未来在神经形态硬件上的实现被讨论为下一步.

关键词:

这是一个ECoG.大脑机器界面是大脑机器界面.发动机控制器解码器神经形态系统是神经形态系统.个性化的神经假肢刺激神经网络的神经网络.

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Neuroplasticity

Neuroplasticity

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