User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features

TitleUser Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features
Publication TypeJournal Article
Year of Publication2019
AuthorsGeorge, L. E., and H. A. Hadi
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume5
Number5
Date Published06/2019
Pagination54-62
Abstract

In this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single channel was investigated in previously published articles. The feature sets considered in previous studies is utilized to establish a combined set of features extracted from two channels. The first feature set is the energy density of power spectra of Discrete Fourier Transform (DFT) or Discrete Cosine Transform; the second one is the set of statistical moments of Discrete Wavelet Transform (DWT). Euclidean distance metric is used to accomplish feature set matching task. The combinations of features from two EEG channels showed high accuracy for the identification system, and competitive results for the verification system. The best achieved identification accuracy is (100%) for all proposed feature sets. For verification mode the best achieved Half Total Error Rate (HTER) is (0.88) with accuracy (99.12%) on Colorado State University (CSU) dataset, and (0.26) with accuracy (99.97%) on Motor Movement/Imagery (MMI) dataset.

KeywordsDCT, Discrete Fourier Transform, Discrete Wavelet Transforms, Electroencephalography, Energy, Euclidean Distance, Statistical Moments
DOI10.9781/ijimai.2018.12.008
URLhttps://www.ijimai.org/journal/sites/default/files/files/2018/12/ijimai_5_5_7_pdf_30776.pdf
AttachmentSize
ijimai_5_5_7.pdf1.17 MB