Multiclass Motor Imagery Recognition of Single Joint in Upper Limb Based on NSGA- II OVO TWSVM

作者：Guan, Shan^*; Zhao, Kai; Wang, Fuwang

来源：Computational Intelligence and Neuroscience, 2018, 2018: 6265108.

DOI：10.1155/2018/6265108

• BRAIN-COMPUTER INTERFACES
• SPATIAL-PATTERNS
• CLASSIFICATION
• BCI
• COMMUNICATION

In the study of the brain computer interface (BCI) system, electroencephalogram (EEG) signals induced by different movements of the same joint are hard to distinguish. This paper proposes a novel scheme that combined amplitude-frequency (AF) information of intrinsic mode IMF) with common spatial pattern (CSP), namely, AF-CSP to extract motor imagery (MI) features, and to improve classification performance, the second generation nondominated sorting evolutionary algorithm (NSGA-II) is used to tune hyperparameters for linear and nonlinear kernel one versus one twin support vector machine (OVO TWSVM). This model is compared with least squares support vector machine (LS-SVM), back propagation (BP), extreme learning machine (ELM), particle swarm optimization support vector machine (PSO-SVM), and grid search OVO TWSVM (GS OVO TWSVM) on our dataset; the recognition accuracy increased by 5.92%, 22.44%, 22.65%, 8.69%, and 5.75%. The proposed method has helped to achieve higher accuracy in BCI systems.

• 出版日期2018
• 单位东北电力大学