Abstract
We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. To select features, we first use the discrete wavelet transform (DWT) or empirical mode decomposition (EMD) to decompose the EEG signals into a set of sub-bands, for which we compute the instantaneous and Teager energy and the Higuchi and Petrosian fractal dimensions for each sub-band. The obtained features are used as input for the local outlier factor (LOF) algorithm to create a model for each subject, with the aim of learning from it and rejecting instances not related to the subject in the model. In search of a minimal subset of EEG channels, we used a channel-selection method based on the non-dominated sorting genetic algorithm (NSGA)-III, designed with the objectives of minimizing the required number EEG channels and increasing the true acceptance rate (TAR) and true rejection rate (TRR). This method was tested on EEG signals from 109 subjects of the public motor movement/imagery dataset (EEGMMIDB) using the resting-state with the eyes-open and the resting-state with the eyes-closed. We were able to obtain a TAR of 1.000 pm 0.000 and TRR of 0.998 pm 0.001 using 64 EEG channels. More importantly, with only three channels, we were able to obtain a TAR of up to 0.993 pm 0.01 and a TRR of up to 0.941 pm 0.002 for the Pareto-front, using NSGA-III and DWT-based features in the resting-state with the eyes-open. In the resting-state with the eyes-closed, the TAR was 0.997 pm 0.02 and the TRR 0.950 pm 0.05, also using DWT-based features from three channels. These results show that our approach makes it possible to create a model for each subject using EEG signals from a reduced number of channels and reject most instances of the other 108 subjects, who are intruders in the model of the subject under evaluation. Furthermore, the candidates obtained throughout the optimization process of NSGA-III showed that it is possible to obtain TARs and TRRs above 0.900 using LOF and DWT- or EMD-based features with only one to three EEG channels, opening the way to testing this approach on bigger datasets to develop a more realistic and usable EEG-based biometric system.
Highlights
We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels
There are a number of approaches that use raw data as input for various configurations of neural networks (NN)[13,14,15,16] and several have been proposed to tackle the high dimensionality of the data using methods for feature extraction, such as principal component analysis (PCA)[17], the discrete wavelet transform (DWT)[3], or empirical mode decomposition (EMD)[2,3,11,12]
We previously showed that the true acceptance rate (TAR) and true rejection rate (TRR) of the models created using one-class support vector machine (OC-SVM) can be improved by finding the best nu and gamma parameters[12]
Summary
We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. In search of a minimal subset of EEG channels, we used a channel-selection method based on the non-dominated sorting genetic algorithm (NSGA)-III, designed with the objectives of minimizing the required number EEG channels and increasing the true acceptance rate (TAR) and true rejection rate (TRR) This method was tested on EEG signals from 109 subjects of the public motor movement/ imagery dataset (EEGMMIDB) using the resting-state with the eyes-open and the resting-state with the eyes-closed. In the resting-state with the eyes-closed, the TAR was 0.997 ± 0.02 and the TRR 0.950 ± 0.05, using DWT-based features from three channels These results show that our approach makes it possible to create a model for each subject using EEG signals from a reduced number of channels and reject most instances of the other 108 subjects, who are intruders in the model of the subject under evaluation. There are a number of approaches that use raw data as input for various configurations of neural networks (NN)[13,14,15,16] and several have been proposed to tackle the high dimensionality of the data using methods for feature extraction, such as principal component analysis (PCA)[17], the discrete wavelet transform (DWT)[3], or empirical mode decomposition (EMD)[2,3,11,12]
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