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.
