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

2018 ◽  
Vol 41 ◽  
pp. 222-232 ◽  
Author(s):  
Duan Li ◽  
Hongxin Zhang ◽  
Muhammad Saad Khan ◽  
Fang Mi
Keyword(s):  
Support Vector Machine ◽  
Spatial Pattern ◽  
Least Squares ◽  
Motor Imagery ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Common Spatial Pattern ◽  
Frequency Selection ◽  
Self Adaptive

Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine

2020 ◽  
Vol 91 (3) ◽  
pp. 034106 ◽  
Author(s):  
Fei Wang ◽  
Zongfeng Xu ◽  
Weiwei Zhang ◽  
Shichao Wu ◽  
Yahui Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Spatial Pattern ◽  
Motor Imagery ◽  
Filter Bank ◽  
Support Vector ◽  
Common Spatial Pattern

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

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Shan Guan ◽  
Kai Zhao ◽  
Fuwang Wang
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Back Propagation ◽  
Classification Performance ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Common Spatial Pattern ◽  
Nsga Ii ◽  
Learning Machine ◽  
Electroencephalogram Eeg

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 function (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.

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