Original Research ARTICLE
Artificial Immune System - Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals
- 1National University of Sciences and Technology, Pakistan
- 2University of Engineering and Technology, Peshawar, Pakistan
Artificial Immune Systems (AIS) are intelligent algorithms derived on the principles inspired by human immune system. In this research work, electroencephalography (EEG) signals for four distinct motor movement of human limbs are detected and classified using Negative Selection Classification Algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV - Graz dataset 2a, comprising 9 subjects) has been used. Mel Frequency Cepstral Coefficients (MFCCs) are extracted as selected feature from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic Algorithm (GA) optimized detectors (Artificial Lymphocytes) are trained using Negative Selection Algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with optimized radius of detector as a non-self detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over 9 subjects with a maximum individual subject classification accuracy of 97.5 % for subject number eight.
Keywords: Brain computer interface (BCI), Artificial immune system, Negative selection algorithm, Stacked auto-encoder, Mel Frequency Cepstral Coefficients (MFCC), electroencephalogram (EEG), Genetic Algorithm
Received: 12 Sep 2018;
Accepted: 08 Oct 2018.
Edited by:Noman Naseer, Air University, Pakistan
Reviewed by:Hendrik Santosa, University of Pittsburgh, United States
Xiaolong Liu, University of Electronic Science and Technology of China, China
Copyright: © 2018 Rashid, Iqbal, Mahmood, Abid, Khan and Tiwana. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mr. Nasir Rashid, National University of Sciences and Technology, Islamabad, Pakistan, email@example.com