EEG signals classification and determination of optimal feature-classifier combination for predicting the movement intent of lower limb

Recent advancements in brain computer-interfacing (BCI) and neuro-robotics have played an indispensable role for people suffering from neural injuries to expect better quality of life by restoring sensory functions and replacement of neuro-muscular pathways as BCI systems work on imagination of movements to control prosthetic limbs. In this research, multiple combinations of features and classifiers have been used to classify electroencephalographic (EEG) signals in order to acquire maximum classification accuracy for a four class EEG based BCI system. Knee and ankle joint movements are executed in a predefined manner during which EEG signals are acquired from sensorimotor cortex of four healthy test subjects, using Emotiv headset. After removing artifacts, five features; average band power, peak, kurtosis, mean and skewness are calculated. Multiple combinations of calculated features are used to classify ankle dorsi-planter flexion and knee extension-flexion movements using five different classifiers; Naïve Bayes, A-nearest neighbor (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). It was found that features set comprising; average band power, peak and mean value yielded significantly higher classification accuracy of 81.31% among all other combinations whereas in classifiers, maximum accuracy of 81.48% is obtained through LDA as compared to other classifiers. These novel findings clearly exhibit the feasibility of achieving good classification accuracies using average band power, mean and peak values of EEG signals as feature vectors and LDA as classifier.