Abstract
The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.
Highlights
The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems
sensorimotor rhythms (SMR) is induced by motor imagery without external stimulation, so it is widely used in BCI systems
Three motor imagery (MI) tasks of the same joint are successfully recognized by using local mean decomposition (LMD)-common spatial pattern (CSP) and MOGWO-TWSVM with seven healthy subjects
Summary
The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. The most classical feature extraction methods include wavelet transform (WT) (You, Chen & Zhang, 2020), empirical mode decomposition (EMD) (Taran et al, 2018), common spatial pattern (CSP) (Yang et al, 2016; Selim et al, 2018), and filter-bank CSP (FBCSP) (Ang et al, 2008; Wang et al, 2020). Malan and Sharma applied dual-tree complex wavelet transform (DTCWT) to extract time, frequency, and phase features of left and right hand MI EEG signals, and classified them using SVM with an average accuracy of 80.7% (Malan & Sharma, 2019). This phenomenon can be recorded by the different electrodes overlying the motor cortex, so BCI systems can efficiently identify MI tasks within different limbs
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