Research on the multi-classifier features of the motor imagery EEG signals in the brain computer interface

Recent research findings in brain computer-interfacing (BCI) have played an indispensable role for stroke patients including people who suffers from neural injuries. This study expects to better quality of life by motor imagery and replacement of neuro-muscular pathways as BCI systems work on motor imagery to control prosthetic limbs for rehabilitation therapy. In this research, multiple combinations of classifiers have been used to classify electroencephalographic (EEG) signals in order to acquire maximum classification accuracy for a three classes EEG based BCI system. Data set IVa from the BCI Competition III was used to evaluate the performance of the proposed algorithm. One-Verous-Rest common spatial patterns(OVR-CSP) is commonly used to extract discriminative spatial filters which can divide the three types of motion imaging tasks into three new two-category classification tasks. We considered using OVR-CSP to extract features together and submitted them to Naive Bayes, LDA, QDA and SVM algorithms which can obviously classify left hand, right hand, and right foot movement. OVR-CSP combined with LDA classifier performed best with an accuracy of 84.62%. Experiments show that these novel findings clearly exhibit the feasibility of achieving good classification accuracies using the covariance matrix of EEG signals as feature vectors and LDA as classifier. Our approach presented in this paper is relatively simple, easy to execute and is validated robustly with a large dataset.