Boi Fabio, Semprini Marianna, Vato Alessandro
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:3052-3055. doi: 10.1109/EMBC.2016.7591373.
Motor brain-machine interfaces (BMIs) transform neural activities recorded directly from the brain into motor commands to control the movements of an external object by establishing an interface between the central nervous system (CNS) and the device. Bidirectional BMIs are closed-loop systems that add a sensory channel to provide the brain with an artificial feedback signal produced by the interaction between the device and the external world. Taking inspiration from the functioning of the spinal cord in mammalians, in our previous works we designed and developed a bidirectional BMI that uses the neural signals recorded form rats' motor cortex to control the movement of an external object. We implemented a decoding interface based on the approximation of a predefined force field with a central attractor point. Now we consider a non-linear transformation that allows to design a decoder approximating force fields with arbitrary attractors. We describe here the non-linear mapping algorithm and preliminary results of its use with behaving rats.
运动脑机接口（BMI）通过在中枢神经系统（CNS）和设备之间建立接口，将直接从大脑记录的神经活动转化为运动指令，以控制外部物体的运动。双向BMI是闭环系统，它增加了一个感觉通道，为大脑提供由设备与外部世界相互作用产生的人工反馈信号。受哺乳动物脊髓功能的启发，在我们之前的工作中，我们设计并开发了一种双向BMI，它利用从大鼠运动皮层记录的神经信号来控制外部物体的运动。我们基于具有中心吸引点的预定义力场近似实现了一个解码接口。现在我们考虑一种非线性变换，它允许设计一个解码器来近似具有任意吸引子的力场。我们在此描述非线性映射算法及其在行为大鼠上使用的初步结果。
