Brainwaves for User Verification using Two Separate Sets of Features based on DCT and Wavelet

Abstract

Abstract-This paper discusses the effectiveness of brain waves for user verification using electroencephalogram (EEG) recordings of one channel belong to single task. The feature sets were previously introduced as features for EEG-based identification system are tested as suitable features for verification system in this paper. The first considered feature set is based on the energy distribution of DCT's or DFT's power spectra, while the second set is based on the statistical moments of wavelet transform, three types of wavelet transforms is proposed. Each set of features is tested using normalized Euclidean distance measure for the matching purpose. The performance of the verification system is evaluated using FAR, FRR, and HTER measures. Two publicly available EEG datasets are used; first is the Colorado State University (CSU) dataset which was collected from seven healthy subjects and the second is the Motor Movement /Imagery (MMI) dataset which is a relatively large dataset was collected from 109 healthy subjects. The attained verification results are encouraging when compared with the results of other recent published works, the best achieved HTER is (0.26) when the system was tested on CSU dataset, while the best achieved HTER is (0.16) when the system was tested on MMI dataset for the features which based on the energy of DFT spectra.