A self-adaptive frequency selection common spatial pattern and least squares twin support vector machine for motor imagery electroencephalography recognition

Publication date: March 2018 Source:Biomedical Signal Processing and Control, Volume 41 Author(s): Duan Li, Hongxin Zhang, Muhammad Saad Khan, Fang Mi Motor imagery brain-computer interface (BCI) systems require accurate and fast recognition of brain activity patterns for reliable communication and interaction. Achieving this accuracy is still a challenge because of the low signal-to-noise ratio in electroencephalography signals and high variability of sensorimotor rhythms. To address this need, we proposed a novel scheme that combined a frequency band selection common spatial pattern algorithm and a particle swarm optimization least squares twin support vector machine classifier for recognition of motor imagery patterns. We used self-adaptive artifact removal and the common spatial pattern method to obtain the most distinguishable features. To improve the classification results, we investigated linear, polynomial, sigmoid, wavelet and gaussian kernel functions for the nonlinear least squares twin support vector machine classifier. Particle swarm optimization, chaotic particle swarm optimization, a genetic algorithm and a quantum genetic algorithm were compared and used to tune the hyper-parameters for the classifier. To evaluate our proposed method, we used BCI Competition IV data sets 2A and 2B. Experimental results showed that for our method, the average recognition accuracy of data set 2A is increased by 6.10%, 6.71%, 3.87%, 4.01%, 2.55% and 4.86% compared with the ...
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research