Journal ArticleDOI
EEG-Based Biometric Authentication Using Gamma Band Power During Rest State
Kavitha P. Thomas,A. P. Vinod +1 more
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TLDR
The proposed authentication technique based on simple cross-correlation values of PSD features extracted from 19 EEG channels during eyes closed and eyes open rest state conditions among 109 subjects offers an equal error rate (EER) of 0.0196 which is better than the state-of-the-art method employing eigenvector centrality features extracting from gamma band of 64 EEG channels of the same dataset.Abstract:
Electroencephalography (EEG), one of the most effective noninvasive methods for recording brain’s electrical activity, has widely been employed in the diagnosis of brain diseases for a few decades. Recently, the promising biometric potential of EEG, for developing person identification and authentication systems, has also been explored. This paper presents the superior performance of power spectral density (PSD) features of gamma band (30–50 Hz) in biometric authentication, compared to delta, theta, alpha and beta band of EEG signals during rest state. The proposed authentication technique based on simple cross-correlation values of PSD features extracted from 19 EEG channels during eyes closed and eyes open rest state conditions among 109 subjects offers an equal error rate (EER) of 0.0196 which is better than the state-of-the-art method employing eigenvector centrality features extracted from gamma band of 64 EEG channels of the same dataset. The obtained results are promising, but further investigation is essential for exploring the subject-specific neural dynamics and stability of gamma waves and for optimizing the results.read more
Citations
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Journal ArticleDOI
A survey on methods and challenges in EEG based authentication
TL;DR: The study shows that the deep learning approaches which are used in the past few years, although still require further research, have shown great results and can be used as a preliminary plan and a roadmap for researchers interested in EEG biometric.
Journal ArticleDOI
Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition.
TL;DR: This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation.
Journal ArticleDOI
EEG-based intention recognition with deep recurrent-convolution neural network: Performance and channel selection by Grad-CAM
TL;DR: A recurrent-convolution neural network model for intention recognition by learning decomposed spatio-temporal representations is introduced and achieves an optimal trade-off between performance and the number of electrode channels for EEG intention decoding.
Journal ArticleDOI
Broad Learning System Based on Maximum Correntropy Criterion
TL;DR: In this paper, the authors adopt the maximum correntropy criterion (MCC) to train the output weights, obtaining a Correntropy-based BLS (C-BLS), which is expected to achieve excellent robustness to outliers while maintaining the original performance of the standard BLS in the Gaussian or noise-free environment.
Journal ArticleDOI
EEG electrode selection for person identification thru a genetic-algorithm method.
TL;DR: This work aimed to determine the minimum set of electrodes required for optimum identification accuracy in each EEG sub-band of both stimuli, and the results were encouraging and it was possible to accurately identify a subject using about 10 out of 64 electrodes.
References
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Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
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Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation
TL;DR: The use of brain activity for person authentication is investigated and a statistical framework based on Gaussian mixture models and maximum a posteriori model adaptation, successfully applied to speaker and face authentication, is proposed, which can deal with only one training session.