My brain is my passport. Verify me

Danko, Amanda S.; Fernandez, Amanda

doi:10.1109/icce.2016.7430507

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Over the years there have been numerous behavioural and physiological characteristics that have been used for biometric applications. They include modalities such as fingerprint, voice, signature, face, iris, DNA, palm‐print, veins, hand geometry [10].…”

Section: Biometricsmentioning

confidence: 99%

Biometric identification using single channel EEG during relaxed resting state

Suppiah

Vinod

2018

IET biom.

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Brain signals have long been studied within various fields like medical, physiotherapy, and neurology for many years. One of the main reasons for this interest is to better understand brain diseases like Parkinson's, Schizophrenia, Alzheimer's, epilepsy, spinal cord injuries, and stroke among others. More recently, they have been used in brain-computer interface systems for rehabilitation, entertainment, and assistance applications. Even with the growing interest in clinical applications, the scientific community has only recently investigated the possibility of using brain signals as a potential biometric feature that can be used in people authentication and recognition systems. In this research, the authors have studied the use of brain signals acquired using electroencephalogram (EEG) during both eyes open and eyes closed states for identification based on a large dataset of 109 subjects. The use of a novel mind relaxation metric to determine the optimum epochs to select for the classification and verification has generated very high classification results, in the range of 97-99% based on a single channel. The approach has also been validated against another dataset to verify its consistency and repeatability. The results demonstrate that it is possible to move towards a single-channel biometric identification system with a very high level of reliability and accuracy.

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Section: Biometricsmentioning

confidence: 99%

Biometric identification using single channel EEG during relaxed resting state

Suppiah

Vinod

2018

IET biom.

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“…The EEG-based subject identification is relatively new. The advantages of using EEG for biometrics are its low exposability (cannot be casually obtained or stolen by external observers) and resistance to forced extraction because under-stress brain activity changes [ 7 ]. They also can be used by disabled patients or users missing some physical trait.…”

Section: Introductionmentioning

confidence: 99%

Combining Cryptography with EEG Biometrics

Damaševičius

Maskeliūnas

Kazanavičius

et al. 2018

Computational Intelligence and Neuroscience

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Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

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