2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Dec. 19, 2016 to Dec. 21, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2016.030
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.
Electroencephalography, Feature extraction, Training, Classification algorithms, Electrodes, Electromyography, Blind source separation,S-RMS, EEG, KNN, IEMG, MAVS, RMS
Umer I. Awan, U.H. Rajput, Ghazaal Syed, Rimsha Iqbal, Ifra Sabat, M. Mansoor, "Effective Classification of EEG Signals Using K-Nearest Neighbor Algorithm", 2016 International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 120-124, 2016, doi:10.1109/FIT.2016.030