01974nas a2200253   4500000000100000000000100001008004100002260001200043653001100055653000800066653002300074653003200097653002700129653003100156653002400187100001600211700001400227245011300241856009600354300001000450490000600460520124000466022001401706       2019                            d  c06/201910aEnergy10aDCT10aEuclidean Distance10aDiscrete Wavelet Transforms10aElectroencephalography10aDiscrete Fourier Transform10aStatistical Moments1 aLoay George1 aHend Hadi00aUser Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features  uhttps://www.ijimai.org/journal/sites/default/files/files/2018/12/ijimai_5_5_7_pdf_30776.pdf  a54-620 v53 aIn 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.  a1989-1660