Abstract
The purpose of this study will be to evaluate the effectiveness on using Electroencephalography (EEG) as a biometric. EEGs are traditionally used in the medical field to record voltage fields to detect and monitor medical conditions. This analysis utilizes several studies that explore different configurations that could be applicable to a biometric application. These studies utilize a Brain-Interface Machine (BIM) in conjunction with neural network classification models. BIM can detect and record voltage fields from the scalp. The voltage field recordings are used as an input to a neural network. The hidden layer of the neural network consists of an algorithm to classify the recordings. Utilizing these concepts, the recommended experiments outline a method to measure the effectiveness of using EEG as a biometric. Each experiment requires the participates to think of an instance of a number or phrase which will then be processed using different neural network configurations. The use of EEG as a biometric variable can be very beneficial as everyone has unique neural measurements, like that of a fingerprint. To create a safe and effective method of authentication, EEG measurements can become a crucial variable in the development of a new authentication scheme making it difficult for hackers to intrude.