Effective Classification of EEG Signals Using K-Nearest Neighbor Algorithm

In this research paper, an effective approach to classify and extract feature vector of different facial expressions and movements recorded using Non-invasive Electroencephalogram (EEG) device is presented. EEG signals were obtained from 10 healthy persons between the age group of 18 to 45. Feature vectors are based on a new approach that make use of Segmentation and Selection (SnS) with Root Mean Square (RMS) to extract feature vector of EEG activities. The classification is done by using K nearest neighbor algorithm. The presented S-RMS (Segmentation and Selection-Root Mean Square) method provides an accuracy of 96.1% and gives better results when compared with others approaches.