Combining Cryptography with EEG Biometrics

Damaševičius, Robertas; Maskeliūnas, Rytis; Kazanavičius, Egidijus; Woźniak, Marcin

doi:10.1155/2018/1867548

Cited by 59 publications

(49 citation statements)

References 48 publications

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“…The increasing awareness of brain–computer interfaces (BCI) for brain signal analysis has sparked new interest in electroencephalogram (EEG) acquisition device development. Various rehabilitation [ 1 ], entertainment, and even security [ 2 ] applications can be implemented by post-processing [ 3 , 4 , 5 ] such electrical signals recorded from the human scalp. However, developing a BCI is a challenging task due to the noisy and variable nature of the EEG signal itself.…”

Section: Introductionmentioning

confidence: 99%

Development of a Modular Board for EEG Signal Acquisition

Uktveris

Jusas

2018

Sensors

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The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0–70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.

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Section: Introductionmentioning

confidence: 99%

Development of a Modular Board for EEG Signal Acquisition

Uktveris

Jusas

2018

Sensors

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“…Therefore, static authentication becomes prey to session hijacking attacks. To overcome the issue of static authentication of biometric features and cryptographic methods, Robertas et al [24] proposed EEG based biometric cryptosystem. This biometric cryptosystem has 4 of low explosibility and can be used by disabled people or users with some missing physical traits.…”

Section: Related Workmentioning

confidence: 99%

“…As per the guidelines of Robertas et al [24], Jorgensen and Yu [52], we tested the app for False Table 4. Survey questions to test the usefulness of the Bio-Guard app.…”

Section: Test For False Rejection Rate (Frr) False Acceptance Rate (mentioning

confidence: 99%

A mobile app to make biometrics usage more secure

Singh¹,

Singh²

2020

Preprint

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With high paced growth in biometrics, its easy availability to capture various biometric features, it is <a>emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security. </a>Organizations encourage their members to use biometrics, but <a>they are hesitant to use due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems. </a>We propose a solution of an extra layer of security, via a low-cost mobile app framework, “Bio-Guard,” to use biometrics, more securely. We tested the app for its design, functions, usability, and it got a score of more than 71% on the usability scale and a reasonably low equal error rate (EER) of 6%. The survey to evaluate the usefulness of the app showed a favorable response of 80%. The results show a good potential of the app to make access to biometric data more secure. The app may enhance users’ confidence-level in encouraging higher participation of users in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many medium and small segment organizations to decrease their information security risk.

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“…Therefore, static authentication becomes prey to session hijacking attacks. To overcome the issue of static authentication of biometric features and cryptographic methods, Robertas et al [24] proposed EEG based biometric cryptosystem. This biometric cryptosystem has low explosibility and can be used by disabled people or users with some missing physical traits.…”

Section: Related Workmentioning

confidence: 99%

Server-specific add-on biometric security layer model 2 to enhance the usage of biometrics

Singh¹,

Singh²

2020

Preprint

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With high paced growth in biometrics, and its easy availability to capture various biometric features, it is <a>emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security. </a>Organizations encourage their members to use biometrics, but <a>they are hesitant to use due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems. </a>We propose a “server-specific add-on biometric security layer model,” to enhance confidence in the usage of biometrics. We tested this model via a biometric mobile app, and the survey showed a favorable response of 80%. The innovative mobile app was tested for its usability and got a score of more than 71%. For test tool reliability, we examined the equal error rate (EER) of the app and got a reasonably low score of 6%. The results show a good potential of this framework to enhance users’ confidence-level in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many organizations to decrease their information security risk.

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Combining Cryptography with EEG Biometrics

Cited by 59 publications

References 48 publications

Development of a Modular Board for EEG Signal Acquisition

Development of a Modular Board for EEG Signal Acquisition

A mobile app to make biometrics usage more secure

Server-specific add-on biometric security layer model 2 to enhance the usage of biometrics

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