EEG-Based User Authentication in Multilevel Security Systems

Pham, Tien; Ma, Wanli; Tran, Dat; Nguyen, Phuoc; Phung, Dinh

doi:10.1007/978-3-642-53917-6_46

Cited by 24 publications

(17 citation statements)

References 18 publications

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“…Frequency-domain analysis has also been employed to extract features from different EEG-based BCIs. Among frequencydomain-based techniques, there are those that use fast Fourier transform (FFT) (Hortal et al, 2015;Bousseta et al, 2018;Yang C. et al, 2018), power spectral density (PSD) (Chiappa and Bengio, 2004;Carlson and Millan, 2013;Mara et al, 2013;Pham et al, 2013;Bascil et al, 2016;Nguyen et al, 2017;Chakladar and Chakraborty, 2018b), band power (Mandel et al, 2009;Serdar Bascil et al, 2015;Kreilinger et al, 2016), and spectral centroid (Murugappan et al, 2014). The PSD of a signal can be calculated through the FFT and Welch's method (Oikonomou et al, 2017).…”

Section: Feature Extraction Approaches In Eeg-based Bci Systemsmentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Section: Feature Extraction Approaches In Eeg-based Bci Systemsmentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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“…The individual periodograms are averaged to reduce the variance of the individual power measurements. This method has been adopted in several studies [112,127,128].…”

Section: Frequency Domainmentioning

confidence: 99%

“…Nonlinear kernels are introduced to map data to another space to make them more separable while increasing the classifier's complexity. One frequently used kernel is the Radial Basis Function (RBF) [30,37,89,106,111,113,127,182]. Other kernels, like sigmoid and polynomial, were also tested [106].…”

Section: Support Vector Machinementioning

confidence: 99%

A Survey on Brain Biometrics

et al. 2019

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Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to spoofing/circumvention, sensitivity to emotional and mental state, continuous nature, and cancelability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure personal identification and authentication. Although existing research on brain biometrics has obtained some intriguing insights, much work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability. CCS Concepts: • Security and privacy → Biometrics; • Computing methodologies → Machine learning approaches; • Applied computing → Psychology;

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“…Error rates were significantly reduced when EEG based multi-factor (EEG, age, and gender) are used for user authentication instead of single factor (EEG only). Another multi-level security system was implemented based on combining different mental tasks for authentication purposes (Pham et al (2013); Palaniappan (2008)). Higher security level was achieved when matching two mental tasks compared to one task only.…”

Section: Related Workmentioning

confidence: 99%