Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Georgi Rusev, Svetlozar Yordanov, Simona Nedelcheva, Alexander Banderov, Fabien Sauter-Starace, Petia Koprinkova-Hristova, Nikola Kasabov
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Abstract

Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too.

目前的脑机接口(BMI)技术解决方案可以达到合理的精度,但大多数系统体积庞大、耗电量大,而且不能自动适应。这项工作要解决的问题是,当前的神经形态技术能否解决这些问题?本文提出了一种新颖的神经形态框架,即通过解码脑皮质图谱(ECoG)信号来控制假肢的 BMI 系统。它包括一个用于脑信号特征提取的三维尖峰定时神经网络(3D-SNN)和一个用于电机控制解码(MCD)的在线可训练递归水库结构(回声状态网络(ESN))。本文介绍了使用 NEST Simulator SNN 库以 Python 编写的软件系统。该系统能够在有监督或无监督模式下实时不断地进行调整。我们在从一名四肢瘫痪者身上获取的多个实验数据集上对所提出的方法进行了测试。首次模拟结果令人鼓舞,同时也表明需要通过多个超参数调整来进一步改进。此外,还讨论了未来在体积更小、功耗更低的神经形态硬件平台上的实施方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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