Electroencephalographic feature evaluation for improving personal authentication performance

Kang, Jae-Hwan; Jo, Young Chang; Kim, Sung-Phil

doi:10.1016/j.neucom.2018.01.074

Cited by 28 publications

(29 citation statements)

References 33 publications

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“…It is known that various phenomena in the natural world contain chaotic characteristics. The chaotic characteristics of biological signals have been studied [36,37] and have recently been utilized, for example, in autism assessment [38] and in person authentication [39].…”

Section: Introduction Of Nonlinear Featuresmentioning

confidence: 99%

Biometrics Using Electroencephalograms Stimulated by Personal Ultrasound and Multidimensional Nonlinear Features

Nakanishi

Maruoka

2019

Electronics

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Biometrics such as fingerprints and iris scans has been used in authentication. However, conventional biometrics is vulnerable to identity theft, especially in user-management systems. As a new biometrics without this vulnerability, brain waves have been a focus. In this paper, brain waves (electroencephalograms (EEGs)) were measured from ten experiment subjects. Individual features were extracted from the log power spectra of the EEGs using principal component analysis, and verification was achieved using a support vector machine. It was found that, for the proposed authentication method, the equal error rate (EER) for a single electrode was about 22–32%, and that, for a multiple electrodes, was 4.4% by using the majority decision rule. Furthermore, nonlinear features based on chaos analysis were introduced for feature extraction and then extended to multidimensional ones. By fusing the results of all electrodes when using the proposed multidimensional nonlinear features and the spectral feature, an EER of 0% was achieved. As a result, it was confirmed that individuals can be authenticated using induced brain waves when they are subjected to ultrasounds.

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Section: Introduction Of Nonlinear Featuresmentioning

confidence: 99%

Biometrics Using Electroencephalograms Stimulated by Personal Ultrasound and Multidimensional Nonlinear Features

Nakanishi

Maruoka

2019

Electronics

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“…In this scheme, L is defined as the signal x (n) length and F is the sampling frequency, respectively. In fact, the PSD value should be calculated at point N. e periodic estimation of the PSD method is expressed as follows: [14] Eye blinking and self-or non-self-rapid serial visual presentation Machine learning 3 -0.9076 [15] Relax and eye-closed Machine learning 60 0.0073 0.9893 [16] MI-EEG 1DCNN-LSTM 1 0.0041 0.995 [17] Imaginary speech Deep learning Four words -0.97 [18] Relax and eye-closed Attention-RNN 1/128 -0.998 [9] Emotion video (different stimulant) 2DCNN + LSTM 12 -1…”

Section: Differential Entropy (De)mentioning

confidence: 99%

“…e average accuracy rate of the study cases reached 97.60%, the false acceptance rate (FAR) was 2.71%, and the false rejection rate (FRR) was 2.09%. Kang et al [15] used an open-access motion-image EEG database for identification.…”

Section: Introductionmentioning

confidence: 99%

DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography

Wang

et al. 2020

Computational and Mathematical Methods in Medicine

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In the past few decades, identification recognition based on electroencephalography (EEG) has received extensive attention to resolve the security problems of conventional biometric systems. In the present study, a novel EEG-based identification system with different entropy and a continuous convolution neural network (CNN) classifier is proposed. The performance of the proposed method is experimentally evaluated through the emotional EEG data. The conducted experiment shows that the proposed method approaches the stunning accuracy (ACC) of 99.7% on average and can rapidly train and update the DE-CNN model. Then, the effects of different emotions and the impact of different time intervals on the identification performance are investigated. Obtained results show that different emotions affect the identification accuracy, where the negative and neutral mood EEG has a better robustness than positive emotions. For a video signal as the EEG stimulant, it is found that the proposed method with 0–75 Hz is more robust than a single band, while the 15–32 Hz band presents overfitting and reduces the accuracy of the cross-emotion test. It is concluded that time interval reduces the accuracy and the 15–32 Hz band has the best compatibility in terms of the attenuation.

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“…For EEG-based biometric systems, several approaches have been presented using various paradigms to stimulate and record the EEG signals, i.e. imagined speech [1][2][3] , resting-state [4][5][6][7][8][9][10] , and event-related potentials (ERPs) 11,12 .…”

mentioning

confidence: 99%

“…Several approaches have been proposed for the creation of a biometric system following various experiment configurations, with various paradigms and methods for feature extraction and classification using the public EEG Motor Movement/Imagery Dataset (EEGMMIDB), using various configurations of neural networks 14,[18][19][20] , other supervised and unsupervised techniques 5,[21][22][23][24][25][26][27][28][29][30][31] , and methods for EEG channel selection 6,32,33 .…”

mentioning

confidence: 99%

Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection

Moctezuma

Molinas

2020

Sci Rep

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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 $$1.000 \pm 0.000$$ 1.000 ± 0.000 and TRR of $$0.998 \pm 0.001$$ 0.998 ± 0.001 using 64 EEG channels. More importantly, with only three channels, we were able to obtain a TAR of up to $$0.993 \pm 0.01$$ 0.993 ± 0.01 and a TRR of up to $$0.941 \pm 0.002$$ 0.941 ± 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 $$0.997 \pm 0.02$$ 0.997 ± 0.02 and the TRR $$0.950 \pm 0.05,$$ 0.950 ± 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.

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Electroencephalographic feature evaluation for improving personal authentication performance

Cited by 28 publications

References 33 publications

Biometrics Using Electroencephalograms Stimulated by Personal Ultrasound and Multidimensional Nonlinear Features

Biometrics Using Electroencephalograms Stimulated by Personal Ultrasound and Multidimensional Nonlinear Features

DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography

Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection

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