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
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