Performance analysis of left and right lower limb movement classification from EEG

The primary prerequisite for development of foot prosthetics driven by brain computer interface (BCI) is classification of left and right lower limb movement from brain signals. Moreover, it is essential to detect best possible combination of feature extraction and classification technique which will efficiently recognize left and right lower limb movement intentions from brain signals in as minimum time as possible. Thus, directed towards finding a solution to these problems, this study is aimed at determining most efficient feature extractor and classifier for recognizing left-right leg movement from brain signals acquired using electroencephalography (EEG). The acquired EEG signals are preprocessed and relevant features are extracted using Power Spectral Density (PSD), statistical parameters (mean, standard deviation, and power) and Hjorth parameter. These extracted features are classified using kNN (k Nearest Neighbor) and SVM (Support Vector Machine). Various parameters like classification accuracy, specificity, sensitivity, and run time are computed for analyzing the performance of the classifiers. kNN and SVM are also statistically analyzed along with two more classifiers (Linear Discriminant Analysis and Quadratic Discriminant Analysis) by Friedman Test. The best accuracy of 90% and sensitivity of 89% is obtained by kNN using Hjorth features taking least time of 78ms. The classifier results are also statistically validated by Friedman Test.