A brain-computer interface decoding method based on spiking neural network

Abstract

The invention discloses a brain-computer interface decoding method based on an impulse neural network, comprising: (1) constructing a liquid state machine model based on an impulse neural network, and the liquid state machine model is composed of an input layer, an intermediate layer and an output layer; wherein, The connection weight from the input layer to the intermediate layer is W hx , the weight of the recurrent connection inside the middle layer is W hh , the read weight from the middle layer to the output layer is W yh ; (2) Input the pulse EEG signal, and train each weight using the following strategies: (2-1) Adopt STDP unsupervised training to connect the weight W hx ; (2-2) Set the intermediate layer cyclic connection weight W by means of distance model and random connection hh ; (2‑3) Using Ridge Regression to train the connection weights W with supervised supervision yh , and establish the mapping between the intermediate layer liquid information R(t) and the output motion information Y(t), and finally output the predicted motion trajectory. By using the present invention, the model can be quickly trained in a relatively short time, the movement trajectory of the arm can be predicted in real time, and the efficiency and accuracy can be improved.

Description

technical field [0001] The invention belongs to the field of invasive action potential brain signal analysis, in particular to a brain-computer interface decoding method based on an impulse neural network. Background technique [0002] The Brain Machine Interface (BMI) system is a method that directly establishes the connection path between the brain and external devices without relying on the human muscle system. BMI is responsible for collecting and analyzing the electrical signals of neural activity output by the brain, and converting them into control signals for external devices or prosthetics (computer cursors, robotic arms, etc.), so that the human brain can directly interact with the device and bypass the nervous and muscular systems. The ultimate goal of the development of BMI is to restore the loss of human motor function in patients due to accidental injury. [0003] At present, with the development of invasive multi-electrode array technology, the BMI system has...

AI Technical Summary

Problems solved by technology

[0007] At present, there is no algorithm for decoding animal movement trajectories based on motor nerve signals of spiking neural networks

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