Combined Optimization of Frequency Band and Time Segment Using Quantum Particle Swarm Algorithm for Brain-Computer Interfaces

Abstract

Common spatial pattern (CSP) is a popular algorithm for spatial filtering and subsequent feature extraction in motor imagery-based (MI) brain-computer interface (BCI) systems. The performance of CSP, however, depends heavily on the subject-specific frequency band and time segment used for classifying mental tasks. Accurate selection of most informative frequency band and time segment poses a great challenge. In this study, quantum particle swarm optimization is proposed for sole selection of frequency band and joint selection of frequency band and time segment, which are realized by a wrapping approach, incorporating CSP for feature extraction and support vector machine for classification into the classification model. The classification error rate is used as the fitness function of quantum particle swarm optimization. The classification performance of quantum particle swarm optimization based CSP algorithm for joint selection of frequency band and time segment is evaluated by comparing with other three CSP algorithms using either fixed frequency band and time segment or fixed time segment and the frequency band selected by particle swarm optimization and quantum-behaved particle swarm optimization, on two MI data sets with different number of channels and trials. Experimental results suggest that the proposed algorithm outperforms the other three algorithms in terms of classification error rate. Across all subjects from the two data sets, the averaged error rate of the proposed algorithm was 7.45%, 2.97% and 2.05% lower than the CSP with fixed frequency band and time segment, that with selected frequency bands by particle swarm optimization and that with selected bands by quantum particle swarm optimization. The proposed algorithm can facilitate the real-world application of BCIs.