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

Background 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. Methods 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. Results 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 O2 as the optimum channel with an accuracy of 95%. Conclusions Channel optimization can obtain higher performance by reducing the number of EEG channels and defined the optimum electrode placement for different mental activities.

[1]  J. F. Alonso,et al.  Stress assessment based on EEG univariate features and functional connectivity measures , 2015, Physiological measurement.

[2]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[3]  Patrizio Campisi,et al.  Brain waves for automatic biometric-based user recognition , 2014, IEEE Transactions on Information Forensics and Security.

[4]  S. Sasikala,et al.  A novel memetic algorithm for discovering knowledge in binary and multi class predictions based on support vector machine , 2016, Appl. Soft Comput..

[5]  Shinji Watanabe,et al.  High-accuracy user identification using EEG biometrics , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[6]  Charles Wang,et al.  I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves , 2013, Financial Cryptography Workshops.

[7]  Patrizio Campisi,et al.  On the vulnerability of an EEG-based biometric system to hill-climbing attacks algorithms' comparison and possible countermeasures , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[8]  Mohammed Abo-Zahhad,et al.  State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals , 2015, IET Biom..

[9]  A. Prasad Vinod,et al.  Online Biometric Authentication Using Subject-Specific Band Power features of EEG , 2017, ICCSP '17.

[10]  A. N. Paithane,et al.  Novel approach for stress recognition using EEG signal by SVM classifier , 2017, 2017 International Conference on Computing Methodologies and Communication (ICCMC).

[11]  S. Kara,et al.  Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure , 2009, Annals of Biomedical Engineering.

[12]  Z. Jane Wang,et al.  Hashing the mAR coefficients from EEG data for person authentication , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Yuan-Ting Zhang,et al.  Heartbeats Based Biometric Random Binary Sequences Generation to Secure Wireless Body Sensor Networks , 2018, IEEE Transactions on Biomedical Engineering.

[14]  Heye Zhang,et al.  Quantitative Assessment for Self-Tracking of Acute Stress Based on Triangulation Principle in a Wearable Sensor System , 2019, IEEE Journal of Biomedical and Health Informatics.

[15]  Li Tong,et al.  EEG-Based Identity Authentication Framework Using Face Rapid Serial Visual Presentation with Optimized Channels , 2018, Sensors.

[16]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[17]  José del R. Millán,et al.  Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Danilo P. Mandic,et al.  EEG Based Biometric Framework for Automatic Identity Verification , 2007, J. VLSI Signal Process..

[19]  Nasreen Badruddin,et al.  Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach , 2017, Medical & Biological Engineering & Computing.

[20]  Rubita Sudirman,et al.  Feature extraction of EEG signal using wavelet transform for autism classification , 2015 .

[21]  Heye Zhang,et al.  Assessment of Biofeedback Training for Emotion Management Through Wearable Textile Physiological Monitoring System , 2015, IEEE Sensors Journal.

[22]  Heung-Il Suk,et al.  Person authentication from neural activity of face-specific visual self-representation , 2013, Pattern Recognit..

[23]  Ramaswamy Palaniappan,et al.  Two-Stage Biometric Authentication Method Using Thought Activity Brain Waves , 2008, Int. J. Neural Syst..

[24]  Jianfeng Hu,et al.  Method of Individual Identification Based on Electroencephalogram Analysis , 2009, 2009 International Conference on New Trends in Information and Service Science.

[25]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[26]  Pietro Liò,et al.  A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine , 2010 .

[27]  Garima Bajwa,et al.  Neurokey: Towards a new paradigm of cancelable biometrics-based key generation using electroencephalograms , 2016, Comput. Secur..