Classification of lower limb motor imagery using K Nearest Neighbor and Naïve-Bayesian classifier

Abstract

For development of foot prosthetics driven by brain computer interface (BCI) for lower limb amputees, the primary requirement is the classification of right and left lower limb motor imagery movement from brain signals. It is important to detect best possible combination of feature extraction and classification algorithms efficiently and accurately recognize left and right lower limb motor imagery from Electroencephalogram (EEG) signals in minimum time possible. An optimal choice has to be reached to select a feature extraction and classification technique with highest accuracy in minimum time. Thus, in this study we direct our attention towards finding the best feature extraction technique and classifier. Preprocessing of the EEG signals are done and relevant features are extracted. The extracted features are then used to classify left and right imagery movement by k-Nearest Neighbor (kNN) and Naive-Bayesian classifier. The best classification accuracy of 90% is obtained by kNN for power spectral density feature set requiring a time of 0.0531 sec. Thus, in future, it can be applied in real time classification to obtain best results in minimum time.