Analysis of Factors Impacting the Efficiency of EEG- Based Biometric Authentication System

Biometric authentication deals with the verifying identity based on physical and behavioural biometric traits of an individual. To prevent against the imposter attack, biometric modalities for person recognition is necessary. The biometrics traits like voice, speech, face, fingerprint recognition systems are commonly used and they are very effective. The disadvantage while using a traditional biometric feature is that they can be easily trapped and deceived. Brain-computer interface is the rapid developing technology, as a new biometric trait., EEG signal has been extensively concerned in recent times. The fact that electroencephalogram signals are distinctive and considerably more challenging to replicate than traditional biometrics which motivated for utilizing them. Indeed, by employing EEG signals obtained from brainiac processes, current user authentication security risks like shoulder surfing can be addressed., offers a novel solution to current security issues involving traditional knowledge-based user identification. In this paper, the objective is to analyze the research work on the EEG based authentication system based on experimental arrangement for users like EEG-acquisition protocols (e.g.., rest, external stimuli driven, mental, and hybrid), utilization of artificial intelligence methodologies and the considerations for security and privacy preservation. The papers examined a wide range of experiment protocols and various algorithms for EEG signal classification. Furthermore, this paper compares various classification and validation techniques. It also focuses on the efficient and effective EEG-based User Authentication techniques., and all the unholding privacy.