Quantum-inspired Evolutionary Algorithm for Feature Selection in Motor Imagery EEG Classification

In Brain-Computer Interfaces, one of the most relevant tasks is the selection of a subset of features that efficiently describes the EEG signal, excluding redundant and irrelevant features. This procedure reduces the dimensionality of the dataset (avoiding the dimensionality curse) and improves the classification accuracy of the system. One of the most successful models applied for this task is the use of an Evolutionary Algorithm in a wrapper approach. These models produce excellent results but present the drawback of a considerable high processing time, a critical limitation for its application on real Brain-Computer Interfaces (BCI) systems. Quantum-inspired Evolutionary Algorithms can be an alternative wrapper approach for the feature selection task, given that they outperform classical Evolutionary Algorithms in the exploration and exploitation of the search space, obtaining the global solution much faster. These algorithm employs concepts and principles from the Quantum Mechanics to probabilistically describe a set of different states between the classical logic states 0 and 1. In this paper, a Quantum-inspired Evolutionary Algorithm is developed and tested over three different subjects from publicly available datasets. In the proposed model, Wavelet Packet Decomposition is employed to analyze the time-frequency characteristics of the signals, and a Multilayer Perceptron Neural Network is employed as a classifier.