CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification

Ruiz-Blondet, Maria V.; Jin, Zhanpeng; Laszlo, Sarah

doi:10.1109/tifs.2016.2543524

Cited by 130 publications

(91 citation statements)

References 40 publications

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“…10 Over the years, several approaches have been proposed to provide a simplified quantitative description of white matter connections, to allow for crosspopulation inferences [12, 13,14,15]. While numerous studies have focused on elucidating brain connectivity patterns that are shared across people, researchers have also acknowledged the high individual variability in brain struc-15 ture [16,17,18], function [19,20,21,22,23,24], and white matter geometry [25,26]. Motivated by this, the concept of connectome fingerprinting, which characterizes individuals using unique connectivity profiles, has recently drawn significant interest from the neuroscience community [27,28,29,30,31,32,33].…”

Section: Introductionmentioning

confidence: 99%

Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

et al. 2017

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Section: Introductionmentioning

confidence: 99%

Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

et al. 2017

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“…Ruiz- Blondet et al (2016) focused on the EventRelated Potential (ERP), arguing that it provides highly accurate biometric recognition. Describing Cognitive Event Related Biometric Recognition (CEREBRE) as an ERP protocol in their work, it is designed to extract unique individual responses from brain.…”

Section: Related Workmentioning

confidence: 99%

“…We have found that Support Vector Machine (SVM) classifier has the highest usage rate. SVM is used in (Ruiz-Blondet et al, 2016;Pham et al, 2015;Kang et al, 2016) and (Bashar et al, 2016) with 100% maximum accuracy rate. Some of the other classifiers mentioned in the related work section of this review paper also have high accuracy rate but their usage in the studies is low compared to SVM.…”

Section: Feature Extractionmentioning

confidence: 99%

A Review Study of Brian Activity-Based Biometric Authentication

Alariki¹,

Ibrahimi²,

Wardak³

et al. 2018

Journal of Computer Science

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Biometrics is the process of identifying an individual among others by biological means. Concerning security, biometric system is one of the best options available in this technology driven era. Places such as nuclear facilities, airports, banks etc. are on the frontline of security threats. Therefore, biometrics such as Iris, face and fingerprint recognition is frequently used to avoid any security breach. However, the possibility of imitating, replicating or even the stealing of original data has made these tools unreliable. As a result, there has been a growing interest in finding a better biometric system and brain activitybased biometrics such as Electroencephalography (EEG) and Functional Near-Infrared Spectroscopy (fNIRS) come with the advantage of being quite impossible to mimic. This paper presents a thorough and in depth review of the state of the art studies and research on brain activity-based biometrics. These studies and selected research projects are reviewed based on their feature extraction, methods, classification and most importantly, performance. Reviewing the most recent studies and research, we have found that time and frequency based features are better to be considered together for a brain activity-based biometric system. Together they are effective and efficient and give us a higher performance rate. Furthermore, we have found that Support Vector Machine (SVM) classifier is the best classification option with 100% accuracy and it can be used for a higher number of users for a biometric system. Our review lays a foundation for future investigation into the use of a combination of EEG and fNIRS for a biometric based authentication system.

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“…Came the request in mind, creates a unique brain signal which is recognizable for an intelligent computational system. 10,11 Various techniques have been developed to extract brain signals which include magneto electroencephalography (MEG), functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS), electrocorticography (ECoG) and electroencephalography (EEG).2 EEG has advantages over other techniques, the most important of which is its good temporal resolution.12 Systems for recording EEG signal are also non-invasive, inexpensive, free of any radiation, and can be simply implemented.2 Thus, in order to capture motor imaginary brain activities in a BCI system, the EEG signal is commonly used.13 An EEG-based BCI system uses EEG as the control signal in neural and muscular free interaction of human with surrounding.1 In different parts of the EEG-based BCI system, the user's EEG signals captured by the electrodes and to decode and recognize the intended interactions are sent to the processor. …”

mentioning

confidence: 99%

mentioning

confidence: 99%

An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals

Abbaspour

Mehrshad

Razavi

2018

Int Clin Neurosci J

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Introduction A brain-computer interface (BCI) system enables nerve and muscle free interaction of human with surrounding.1 Some recent and important research applications of BCI are human to human interface, neuroprosthetics, exoskeleton control, mobile and guided robotics, biometrics, neurogaming and Intelligent Transportation. [2][3][4][5] Due to the wide range of applications, BCI systems have the potential to use by both normal and disabled individual.6 Using this technology, severely disabled, paralyzed or who have neuromuscular diseases such as amyotrophic lateral sclerosis, stroke or spinal cord injuries become self-sufficient in fulfilling their basic requirements and severe motor disabilities. 7,8 Actually, BCI systems increase the quality of their life while reduce the burden and cost of care. Came the request in mind, creates a unique brain signal which is recognizable for an intelligent computational system. 10,11 Various techniques have been developed to extract brain signals which include magneto electroencephalography (MEG), functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS), electrocorticography (ECoG) and electroencephalography (EEG).2 EEG has advantages over other techniques, the most important of which is its good temporal resolution.12 Systems for recording EEG signal are also non-invasive, inexpensive, free of any radiation, and can be simply implemented.2 Thus, in order to capture motor imaginary brain activities in a BCI system, the EEG signal is commonly used.13 An EEG-based BCI system uses EEG as the control signal in neural and muscular free interaction of human with surrounding.1 In different parts of the EEG-based BCI system, the user's EEG signals captured by the electrodes and to decode and recognize the intended interactions are sent to the processor. 14The main purpose of EEG-based BCI is to translate EEG signal into a command for a computer.14 Generally, the design of such a system is very complex. 9 In many researches that have been done so far on EEG-based BCI systems, actually the system is reduced to a classifier

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CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification

Cited by 130 publications

References 40 publications

Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

A Review Study of Brian Activity-Based Biometric Authentication

An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals

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