ReGA based feature selection emotion recognition using EEG signals

In recent years, with the development of computer technology, EEG emotion recognition has been paid much attention in the field of emotional computing and has become a hotspot. In the practical application of EEG emotion recognition, it is not only required to ensure the accuracy rate, but also the operation efficiency. Based on this, we propose a ReGA algorithm to select the EEG characteristics. In the ReGA algorithm, the ReliefF algorithm is used to calculate the feature weight, and the heuristic information is provided for the population initialization of the encapsulation stage genetic algorithm, so that the initial population contains a good starting point, so the genetic algorithm can adopt less evolution Algebra and small-scale populations to find a better subset of features. Therefore, the ReGA algorithm can avoid the danger of ReliefF to remove important features, but also reduce the probability of genetic algorithm to adapt and improve the efficiency of computing.