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

• Published in: Review of scientific instruments Vol. 91; no. 3; pp. 034106 - 34115
• Main Authors: Wang, Fei, Xu, Zongfeng, Zhang, Weiwei, Wu, Shichao, Zhang, Yahui, Ping, Jingyu, Wu, Chengdong
• Format: Journal Article
• Language: English
• Published: United States American Institute of Physics 01.03.2020
• Subjects: Brain-Computer Interfaces; Classification; Correlation coefficients; Covariance matrix; Electroencephalography; Euclidean geometry; Euclidean space; Feature extraction; Filter banks; Frequencies; Human-computer interface; Humans; Image classification; Mathematical analysis; Matrix methods; Riemann manifold; Scientific apparatus & instruments; Signal Processing, Computer-Assisted; Support Vector Machine; Support vector machines
• ISSN: 0034-6748; 1089-7623; 1089-7623
• DOI: 10.1063/1.5142343

In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automobiles, entertainment, and so on. Motor imagery relevant electroencephalogram (EEG) signals are weak, nonlinear, and susceptible to interference. As a feature extraction method for motor imagery, Common Spatial Pattern (CSP) has been proven to be very effective. However, its effectiveness depends heavily on the choice of frequency bands, and Euclidean space cannot effectively describe the inner relationship. To solve these problems, a classification approach for motor imagery using the Geodesic Filtering Common Spatial Pattern (GFCSP) and filter-bank Feature Weighted Support Vector Machine (FWSVM) is presented. First, GFCSP based on the Riemannian manifold is proposed, in which the extracted covariance features are spatially filtered in Riemannian tangent space, and the average covariance matrix is replaced by Riemannian mean in CSP. Second, filter-bank FWSVM with a feature weighted matrix is proposed. EEG signals are filtered into 8–12 Hz, 12–16 Hz, 18–22 Hz, 22–26 Hz, and a wide band of 8–24 Hz, and GFCSP features of these filtered signals are extracted. A feature weighted matrix is calculated using mutual information and the Pearson correlation coefficient from these features and class information. Then, the Support Vector Machine (SVM) is used for classification with the feature weighted matrix. Finally, the proposed method is validated on the dataset IVa in BCI competition III. Classification accuracies of the five subjects are 92.31%, 99.03%, 80.36%, 96.30%, and 97.67%, which demonstrate the effectiveness of our proposed method.