EEG-Based Person Authentication Using a Fuzzy Entropy-Related Approach with Two Electrodes

Zhang, Mu; Hu, Jianfeng; Min, Jianliang

doi:10.3390/e18120432

Cited by 60 publications

(33 citation statements)

References 38 publications

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“…Mu, Hu and Min enhanced the performance of EEG biometric using feature extraction based on fuzzy entropy, and feature selection based on Fisher distance. In their experiment, data was captured from 10 subjects using two electrodes, namely, FP1 and FP2.…”

Section: Emerging Biometric Technologiesmentioning

confidence: 99%

State of the art and perspectives on traditional and emerging biometrics: A survey

Traoré

Alshahrani

Obaidat

2018

Security and Privacy

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The last three decades have seen a shift and impressive progress in the biometric technologies landscape. Several major real‐world applications of biometrics have been released and are currently being used around the world. At the same time, several new biometrics modalities have emerged and have started making an impact in securing sensitive data, information, and systems. We present in this paper a survey of traditional and emerging biometrics by emphasizing their connection to human body parts, behavior, and cognition. A review of the state of the art in the key technologies is carried out along with a discussion of major applications currently available in the real world.

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Section: Emerging Biometric Technologiesmentioning

confidence: 99%

State of the art and perspectives on traditional and emerging biometrics: A survey

Traoré

Alshahrani

Obaidat

2018

Security and Privacy

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“…The final result was achieved by averaging the outcome produced in the corresponding test repeated 10 times (for different subjects and different feature sets). Using this evaluation scheme, the dependency of the training and test features was removed, thus avoiding the over-fitting problem [37][38][39][40][41][42]. In particular, though GB is a more capable and practical boosting algorithm, like most other classifiers, GB also had the problem of over-fitting when dealing with very noisy data.…”

Section: Classificationmentioning

confidence: 99%

“…Based on the literature [36][37][38][39], of the four feature sets, FE out-performed the other feature sets. DT was the best among several classifiers, while SVM was the weakest.…”

Section: Effect Of Noise: Dt Classifier Vs Knn Classifier Vs Svm CLmentioning

confidence: 99%

Noise Robustness Analysis of Performance for EEG-Based Driver Fatigue Detection Using Different Entropy Feature Sets

Wang

2017

Entropy

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Driver fatigue is an important factor in traffic accidents, and the development of a detection system for driver fatigue is of great significance. To estimate and prevent driver fatigue, various classifiers based on electroencephalogram (EEG) signals have been developed; however, as EEG signals have inherent non-stationary characteristics, their detection performance is often deteriorated by background noise. To investigate the effects of noise on detection performance, simulated Gaussian noise, spike noise, and electromyogram (EMG) noise were added into a raw EEG signal. Four types of entropies, including sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for feature sets. Three base classifiers (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT)) and two ensemble methods (Bootstrap Aggregating (Bagging) and Boosting) were employed and compared. Results showed that: (1) the simulated Gaussian noise and EMG noise had an impact on accuracy, while simulated spike noise did not, which is of great significance for the future application of driver fatigue detection; (2) the influence on noise performance was different based on each classifier, for example, the robust effect of classifier DT was the best and classifier SVM was the weakest; (3) the influence on noise performance was also different with each feature set where the robustness of feature set FE and the combined feature set were the best; and (4) while the Bagging method could not significantly improve performance against noise addition, the Boosting method may significantly improve performance against superimposed Gaussian and EMG noise. The entropy feature extraction method could not only identify driver fatigue, but also effectively resist noise, which is of great significance in future applications of an EEG-based driver fatigue detection system.

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“…The authors have shown that using FEn as a feature results in classification accuracy greater than 87.3%, while only two frontal electrodes are required. The study was heavily based on the algorithm's robustness to noise and sensitivity to different levels of signal randomness which eventually aid in the reduction of electrodes and the improvement of classification rates simultaneously [23].…”

Section: Introductionmentioning

confidence: 99%

Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

Amarantidis

Abásolo

2019

Entropy

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Biomedical signals are measurable time series that describe a physiological state of a biological system. Entropy algorithms have been previously used to quantify the complexity of biomedical signals, but there is a need to understand the relationship of entropy to signal processing concepts. In this study, ten synthetic signals that represent widely encountered signal structures in the field of signal processing were created to interpret permutation, modified permutation, sample, quadratic sample and fuzzy entropies. Subsequently, the entropy algorithms were applied to two different databases containing electroencephalogram (EEG) signals from epilepsy studies. Transitions from randomness to periodicity were successfully detected in the synthetic signals, while significant differences in EEG signals were observed based on different regions and states of the brain. In addition, using results from one entropy algorithm as features and the k-nearest neighbours algorithm, maximum classification accuracies in the first EEG database ranged from 63% to 73.5%, while these values increased by approximately 20% when using two different entropies as features. For the second database, maximum classification accuracy reached 62.5% using one entropy algorithm, while using two algorithms as features further increased that by 10%. Embedding entropies (sample, quadratic sample and fuzzy entropies) are found to outperform the rest of the algorithms in terms of sensitivity and show greater potential by considering the fine-tuning possibilities they offer. On the other hand, permutation and modified permutation entropies are more consistent across different input parameter values and considerably faster to calculate.

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EEG-Based Person Authentication Using a Fuzzy Entropy-Related Approach with Two Electrodes

Cited by 60 publications

References 38 publications

State of the art and perspectives on traditional and emerging biometrics: A survey

State of the art and perspectives on traditional and emerging biometrics: A survey

Noise Robustness Analysis of Performance for EEG-Based Driver Fatigue Detection Using Different Entropy Feature Sets

Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

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