Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners

Electroencephalogram is the electrical phenomena in the cerebral cortex or the scalp surface due to the electrophysiological activity of brain cells. Electroencephalogram has great theoretical and practical significance in measuring mental workload of people. More precise electroencephalographic is a precondition to study mental workload of miners. In this article, based on the actual situation of the electroencephalographic measurement of miners, the particle swarm optimization is introduced to improve the standard genetic algorithm, and put forward a combined method integrating the genetic algorithm with particle swarm optimization for achieving electroencephalogram-based measures of miners' mental workload. Moreover, the MATLAB simulation platform is used for simulation testing. Testing results prove the effectiveness of the combined method.