Research Output
Analysis of familiar and unfamiliar images using power spectral estimation for EEG authentication system
Biometric authentication system 8sing EEG biometric trait - A review
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Publication
Biometric authentication is a system used for recognizing an individual according to their physiological and behavioral characteristics, Recently, the application of biometric authentication is the most useful in cybersecurity such as fingerprints, facial, and voices. Traditional authentication such as password and PIN have been used for a decade, however, they bring drawbacks to the users which were attacked by cybercriminals. Therefore, the brainwave of electroencephalogram (EEG) is proposed as the biometric trait to encounter the problems faced. The aim of this study is to explore the feature extraction method by applying the power spectral estimation method as linear feature analysis such as the Welch method, Burg Method, Yule-Walker method, Covariance method and Modified Covariance method. After extracting five features, the statistics of mean, median, variance, and the standard deviation is computed and fed into three types of shallow classifiers including the Neural Network, K-Nearest Neighbors, and Support Vector Machine. As a result, the Yule-Walker feature contributes to the highest average accuracy and the Neural Network using Levenberg-Marquardt (LM) has achieved the highest accuracy for all frequency bands. In fact, the highest frequency band obtained by gamma (30-45 Hz) followed by highbeta (20-30 Hz), lowbeta (13-20 Hz) and alpha (8-13 Hz). Overall, all three features and three classifiers are able to achieve between 70.1% to 98.2% of accuracy which shows that it can differentiate between different tasks.
Publication
Biometric authentication is a recognition of individual according to their unique physiological and behavioural characteristics. Recently, the application of biometric is the most trending in cyber security technology such as fingerprint, facial recognition, and voice recognition. However, these biometrics have their own drawbacks which allow the unauthorized party to cybercrime and the number of cases is also increased. To encounter this kind of problem, the previous researchers proposed brain signal or electroencephalogram (EEG) as biometric trait. EEG is an electrical activity recorded via non-invasive method using electrode placed on the scalps and measured as voltages. EEG is chosen by the researchers as biometric module because EEG hold its own unique characteristics and more robust against the cybercriminals. This paper presents a review of the EEG-based biometric studies and research. The previous research was reviewed based on their signal acquisition, pre-processing, feature extraction and classification. The general knowledge of EEG and the basic operation of biometric authentication also discussed in this paper. The recent studied and research is chosen with various proposed method respect to the better performance rate. In addition, the deep learning in biometric authentication is found to be the popular among the researchers for classification step because more robust and automatically extracted feature within the network.