Biometrics is the process of identifying an individual among others by biological means. Concerning security, biometric system is one of the best options available in this technology driven era. Places such as nuclear facilities, airports, banks etc. are on the frontline of security threats. Therefore, biometrics such as Iris, face and fingerprint recognition is frequently used to avoid any security breach. However, the possibility of imitating, replicating or even the stealing of original data has made these tools unreliable. As a result, there has been a growing interest in finding a better biometric system and brain activitybased biometrics such as Electroencephalography (EEG) and Functional Near-Infrared Spectroscopy (fNIRS) come with the advantage of being quite impossible to mimic. This paper presents a thorough and in depth review of the state of the art studies and research on brain activity-based biometrics. These studies and selected research projects are reviewed based on their feature extraction, methods, classification and most importantly, performance. Reviewing the most recent studies and research, we have found that time and frequency based features are better to be considered together for a brain activity-based biometric system. Together they are effective and efficient and give us a higher performance rate. Furthermore, we have found that Support Vector Machine (SVM) classifier is the best classification option with 100% accuracy and it can be used for a higher number of users for a biometric system. Our review lays a foundation for future investigation into the use of a combination of EEG and fNIRS for a biometric based authentication system.

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