Overt Mental Stimuli of Brain Signal for Person Identification

• Published in: 2016 International Conference on Cyberworlds (CW) pp. 197 - 203
• Main Authors: Rahman, Md Wasiur, Gavrilova, Marina
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2016
• Subjects: Artificial Neural Network (ANN); Back Propagation (BP); Brain signal; Classification; Cybersecurity; Discrete wavelet transforms; Electroencephalogram (EEG); Electroencephalography; Feature extraction; Identification of persons; K Nearest Neighbor (KNN); Overt Mental Stimuli; Support vector machines; The Discrete Wavelet Transform (DWT)
• DOI: 10.1109/CW.2016.41

Cybersecurity is an important and challenging issue faced by governments, financial institutions and ordinary citizens alike. Secure identification is needed for accessing confidential government information, online bank transaction, person's social network (Facebook, Twitter, Linkedin). Brain signal electroencephalogram (EEG) can play a vital role in ensuring security as it is non-vulnerable and hard to steal. In this article, we develop an EEG based biometric security system. The purpose of this work is to find the best band or the best bands combination of overt mental stimuli of brain EEG signal to identify a person. The Discrete Wavelet Transform (DWT) is used to extract different significant features which separate Alpha, Beta and Theta band of frequencies of the EEG signal. Extracted EEG features of different bands and their combinations such as alpha-beta, alpha-theta, theta-beta, alphabeta-theta are classified using an artificial neural network (ANN) trained with the back propagation (BP) algorithm. The classification rate shows that Alpha band (84.4%) has higher mapping precision and better convergence rate than the other bands, beta (80%), theta (78.1%) and bands combination as alpha-beta (64.1%), alpha-theta (65.6%), beta-theta (58.8%), alpha-beta-theta (56.9%). Another classifier K nearest neighbor (KNN) is used to verify this result. The classification result of this KNN classifier also shows that alpha band (50%) has higher convergence rate than other bands, beta (40%) and theta (40%). The results of this study are expected to be helpful for future research of overt mental stimuli brain signal based biometric approaches.