Chaotic Quantum-inspired Evolutionary Algorithm: enhancing feature selection in BCI

Abstract

Quantum-inspired Evolutionary Algorithms (QiEAs) have demonstrated to be very effective in several applications. In particular, employing this algorithm for feature selection as a wrapper technique in Brain-Computer Interfaces applications was recently proposed with great results. Moreover, the training time of the model was decreased while maintaining a high classification accuracy, both essential conditions for a successful BCI. The drawback of this model was the sensitiveness to changes in the direction and magnitude of the rotation angle, which can produce adverse effects in both performance and convergence time. Chaotic systems and Evolutionary algorithms, when combined, can enhance the convergence rate and speed of the evolutionary process, incrementing the capacity of reaching the global optima. In this paper we explore the effects of adding ergodicity to a QiEA by the employment of chaotic maps in two operators: chaotic uniform crossover and chaotic quantum update gate. To validate the proposed approach, six commonly used chaotic maps are tested with data of Motor Imagery (MI) Electroencephalography (EEG) of right and left hand movement. The results of these experiments are compared with the ones of a QiEA and a classical Genetic Algorithm (GA). In the proposed model, Wavelet Packet Decomposition is employed as the time-frequency analysis to characterize the signal, whereas a Multilayer Perceptron Neural Network is used as a classifier. The results demonstrated that Chaotic QiEAs can significantly improve the convergence time of the model with only a small loss in the final accuracy.