Open AccessIdentification of optimum segment in single channel EEG biometric system
Author(s) -
Muhammad Afif Hendrawan,
Pramana Yoga Saputra,
Cahya Rahmad
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v23.i3.pp1847-1854
Subject(s) - biometrics , electroencephalography , pattern recognition (psychology) , support vector machine , daubechies wavelet , artificial intelligence , linear discriminant analysis , computer science , discrete wavelet transform , channel (broadcasting) , signal (programming language) , feature (linguistics) , feature vector , speech recognition , autoregressive model , feature extraction , spectral density , identification (biology) , wavelet , wavelet transform , mathematics , statistics , psychology , telecommunications , linguistics , philosophy , botany , psychiatry , biology , programming language
Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.