Rusev Georgi, Yordanov Svetlozar, Nedelcheva Simona, Banderov Alexander, Sauter-Starace Fabien, Koprinkova-Hristova Petia, Kasabov Nikola
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria.
Univ. Grenoble Alpes, CEA, Leti, F-38000 Grenoble, France.
Biomimetics (Basel). 2025 Mar 14;10(3):183. doi: 10.3390/biomimetics10030183.
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.
当前脑机接口（BMI）的技术解决方案具有合理的准确性，但大多数系统体积庞大、功耗高且不具备自适应能力。这项工作探讨了当前的神经形态技术能否解决这些问题？本文提出了一种用于假肢控制的新型神经形态BMI系统框架，通过解码皮层脑电图（ECoG）脑信号来实现。它包括一个用于脑信号特征提取的三维脉冲时间神经网络（3D-SNN）和一个用于运动控制解码（MCD）的在线可训练递归储层结构（回声状态网络（ESN））。描述了一个使用NEST模拟器SNN库用Python编写的软件系统。它能够在监督或无监督模式下实时连续自适应。所提出的方法在从一名四肢瘫痪患者获取的多个实验数据集上进行了测试。初步模拟结果令人鼓舞，同时也表明需要通过多个超参数调整进一步改进。还讨论了其未来在尺寸更小、功耗显著更低的神经形态硬件平台上的实现。
