Feature Optimization of EEG Signals Based on Ant Colony Algorithm

Abstract

EEG signal can be understood as a kind of bioelectrical signal, which can reflect emotional information when the body is in different emotional states. However, the data collected are often high-dimensional. including many irrelevant or redundant features. The high-dimensional features make the space cost increase exponentially, which brings many difficulties to the research. Ant colony optimization algorithm, a swarm intelligence algorithm, can be used for feature selection. Ant colony optimization algorithm is used for feature selection of EEG signals. The feature subset to be selected is trained cooperatively and learned actively. The classification accuracy is evaluated through convolutional neural network, and the optimal subset is selected from the iterative local optimal solution. The results show that the ant colony optimization algorithm can effectively reduce the time complexity and calculation cost, Improve the accuracy of classification.