Brain signal based biometric identification using one-dimensional local gradient pattern and artificial neural network

Abstract

Biometric identification or recognition refers to the process of identifying an unknown individual based on the physiological or behavioral characteristics. While fingerprint, palm, face belongs to physiological characteristics, traits like voice, gait falls in the category of behavioral characteristics. Recently, there has been an increase in interest in developing neural signal based biometric identification system as the brain signals have certain unique features related to an individual and they are difficult to mimic as well. Electroencephalogram (EEG) captures the brain electrical activity and used in different applications including health care and human-computer interaction. In this paper, a new approach with the combination of one-dimensional Local Gradient Pattern (1D-LGP) and Artificial Neural Network (ANN) has been introduced for building EEG signal based biometric identification system. The proposed framework consists of two steps. In the first step, the 1D-LGP code is computed for each signal point in the EEG signal and the histogram is formed. The histogram represents the extracted feature vector of the corresponding EEG signal which is then fed to the ANN classifier to perform the classification. The experiment has been carried-out with the benchmark dataset having 20 subjects. The system performance has been evaluated using the mean accuracy obtained after 20 runs of 10-fold cross validation. The experimental results show that the proposed technique achieved a high accuracy.