A survey on methods and challenges in EEG based authentication

Abstract EEG is the recording of electrical activities of the brain, usually along the scalp surface, which are the results of synaptic activations of the brain’s neurons. In recent years, it has been shown that EEG is an appropriate signal for the biometric authentication and has important features such as resistance to spoofing attacks and impossibility to use under pressure and coercion states. In this paper, the state-of-the-art methods in EEG based authentication are reviewed. This review includes a number of aspects such as the various tasks that the user required to perform during the authentication, devices and available datasets, the preprocessing procedures and the classification methods used in the EEG biometric authentication. Both shallow and deep classification methods are reviewed in this paper. The study shows that the deep learning approaches which are used in the past few years, although still require further research, have shown great results. Moreover, the paper summarizes the works to address the open challenges of this area. The EEG authentication challenges have been discussed from a variety of points of view, including privacy, user-friendliness, attacks, and authentication requirements such as universality, permanency, uniqueness, and collectability. This paper can be used as a preliminary plan and a roadmap for researchers interested in EEG biometric.