Particle swarm algorithm-based neuron swarm model parameter adaptive optimization method

The invention discloses a particle swarm algorithm-based neuron swarm model parameter adaptive optimization method. The method comprises the steps of S1, initializing a particle swarm, setting basic parameters of the particle swarm algorithm, setting a parameter combination search range of each particle, and setting basic parameters of a neuron swarm model; S2, calculating a fitness value of eachparticle in the initial particle swarm, and initializing a particle individual extremum and a global extremum according to the fitness value of the particle; S3, updating the particle speed and position; s4, calculating a new particle fitness value, and updating a particle individual extreme value and a global extreme value; s5, judging whether the maximum number of iterations is met or not, if yes, outputting globally optimal particles, and otherwise, returning to the step S3; and S6, obtaining an optimal parameter combination of the electroencephalogram frequency band according to the globally optimal particles outp