EEG-based single-channel authentication systems with optimum electrode placement for different mental activities

• Published in: Biomedical Journal Vol. 42; no. 4; pp. 261 - 267
• Main Authors: Zeynali, Mahsa, Seyedarabi, Hadi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Limited 01.08.2019; Chang Gung University; Elsevier
• Subjects: Accuracy; Advantages; Algorithms; Artificial intelligence; Autoregressive models; Bayes Theorem; Bayesian analysis; Biometrics; Brain - physiology; Channels; Classification; Conflicts of interest; Discrete Wavelet Transform; EEG; Electrocardiography; Electrodes; Electroencephalography; Electroencephalography - methods; Entropy; Feature extraction; Fourier transforms; Humans; Machine learning; Methods; Neural networks; Neural Networks, Computer; Optimization; Original article; Placement; Signal Processing, Computer-Assisted; Standard deviation; Support vector machines; Wavelet Analysis; Wavelet transforms; Signal processing; Biometric; Electroencephalogram (EEG); Single-channel; Security; Authentication
• ISSN: 2319-4170; 2320-2890; 2320-2890
• DOI: 10.1016/j.bj.2019.03.005

Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authentication and security is a very important issue in our life and brainwave-based authentication is an addition to biometric authentication systems, which has many advantages over others. In this paper, we study the performance of a single channel brainwave-based authentication systems and select optimum channels based on mental activities. In this study, we used a dataset with five mental activities with seven subjects (325 samples). The EEG based authentication system includes three pre-processing steps, feature extraction, and classification. Features for Subject Authentication, are obtained from discrete Fourier transform, discrete wavelet transform, autoregressive modeling, and entropy features. Then these features are classified using the Neural Network, Bayesian network and Support Vector Machine. We achieved accuracy in the range of 97-98% mean accuracy with Neural Network classifier for single-channel authentication system with optimum electrode placement for mental activity. We also analyzed the authentication system independently from the type of mental activity and chose channel O as the optimum channel with an accuracy of 95%. Channel optimization can obtain higher performance by reducing the number of EEG channels and defined the optimum electrode placement for different mental activities.