Biometric user authentication using brain waves
01 Aug 2016-pp 1-6
TL;DR: The design and implementation of a system which allows user to set a pattern of brain waves which must be provided as an unlock pattern to get the access to two levels of authentication, first level of which is brain waves.
Abstract: Authentication has become an essential part of our everyday lives which is used at almost every place from banks to experimental labs, from car automation to home automation. This authentication is generally provided through systems like passwords, PIN codes, card readers. At some places biometrics like fingerprint and retina scans are used. All designed with one purpose; to confirm a person's identity. Brain wave based authentication is another addition to the wide range of authentication systems, which has many advantages over other authentication systems. With a standard password someone can watch or ‘shoulder-surf’ what others type, but no one ca n watch thoughts. Cards and keys can be lost, but the brain wave is always present. Differently abled persons can't use systems which uses fingerprints or retina scans but they can use system using brain-waves. This clears that using brain waves as biometric to provide authentication is very beneficial. A system is designed and implemented which allows user to set a pattern of brain waves which must be provided as an unlock pattern to get the access. This pattern can be any combination of eye blink, attention and various brain rhythms like Alpha, Beta, Theta and Delta. The system described in this paper provides two-level authentication. First level of which is brain waves. Once the correct pattern of brain signal is provide the system will ask for a pass key as a second level of authentication. This paper describes the design and implementation of the system.
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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.
Abstract: The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. 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. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.
73 citations
01 Aug 2018
TL;DR: In this paper, a drowsiness detection system based on a brain-computer interface (BCI) headset having 3 electrodes is proposed, which is mounted on the user’s head to compute the brain wave frequencies.
Abstract: In this paper, a drowsiness detection system based on a brain-computer interface (BCI) headset having 3 electrodes is proposed. The headset is mounted on the user’s head to compute the brain wave frequencies. The signal received from BCI headset is processed to remove the external noise. The computed frequencies is then compared by the threshold frequencies of the brain state and a particular decision like whether an individual is in an active state or in a drowsy state can be taken. If an individual is in a drowsy state, a particular alarm is generated on his/her Android phone to wake the person up. An individual user’s sleeping pattern while working is stored on the cloud so that it can be used for analyzing an individual’s brain state while working. This project is proposed with an aim of getting the work done within a particular time period.
14 citations
Cites background from "Biometric user authentication using..."
...That is why the EEG signals are also termed as brain waves [1]....
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...All electrical activity of brain is recorded from the scalp surface at a particular position [1], thus EEG can be applied repeatedly without any risk to anyone....
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27 Jul 2018
TL;DR: Results in terms of accuracy reached high levels renders the proposed approach suitable for direct assessment of the driver’s mental state for road safety and accident avoidance in a potential in-vehicle smart system.
Abstract: Alcohol consumption affects the function of the brain and long-term excessive alcohol intake can lead to severe brain disorders. Wearable electroencephalogram (EEG) recording devices combined with Brain Computer Interface (BCI) software may serve as a tool for alcohol-related brain wave assessment. In this paper, a method for mental state assessment from alcohol-related EEG recordings is proposed. EEG recordings are acquired with the Emotiv EPOC+, after consumption of three separate doses of alcohol. Data from the four stages (alcohol-free and three levels of doses) are processed using the OpenViBE platform. Spectral and statistical features are calculated, and Grammatical Evolution is employed for discrimination across four classes. Obtained results in terms of accuracy reached high levels (89.95%), which renders the proposed approach suitable for direct assessment of the driver’s mental state for road safety and accident avoidance in a potential in-vehicle smart system.
11 citations
Cites background from "Biometric user authentication using..."
...Moreover, driver authentication from brain waves can be integrated into a driver’s authentication system [5,6] and thus BCI systems for road safety have been into consideration [7–9]....
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Posted Content•
TL;DR: In this article, the authors present the key concepts of the main areas in computer security and how it has evolved in the last years, focusing on cryptography, user authentication, denial of service attacks, intrusion detection and firewalls.
Abstract: The new generation of security threats has been promoted by digital currencies and real-time applications, where all users develop new ways to communicate on the Internet. Security has evolved in the need of privacy and anonymity for all users and his portable devices. New technologies in every field prove that users need security features integrated into their communication applications, parallel systems for mobile devices, internet, and identity management. This review presents the key concepts of the main areas in computer security and how it has evolved in the last years. This work focuses on cryptography, user authentication, denial of service attacks, intrusion detection and firewalls.
5 citations
TL;DR: The results showed that the most stable was the α-rhythm pattern in the resting state with the eyes closed, with minimal values of the coefficient of variation of the α rhythm but strong within-group spreads.
Abstract: This article addresses the main problems in person authentication using the EEG. This area is currently under active development due to advances in virtual spaces and seeking new methods for user recognition in different internet platforms. One task that needs to be solved is that of identifying stable EEG measures and patterns which might be used to perform reliable recognition of people over long time intervals. The second question considered here is that of selecting tasks for EEG recording protocols. In the present study, subjects’ EEG traces were recorded at rest and on performance of various motor tasks five times over three months and the stability of the different patterns was then compared. The results showed that the most stable was the α-rhythm pattern in the resting state with the eyes closed, with minimal values of the coefficient of variation of the α rhythm but strong within-group spreads. Of the active tests, the most stable indicators were obtained on observing motor actions and the least stable patterns were seen on performance. Writing with a pen was the action characterized by the lowest stability of EEG measures.
4 citations
References
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276 citations
TL;DR: The issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications are speculated on and a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition is provided.
Abstract: Brain signals have been investigated within the medical field for more than a century to study brain diseases like epilepsy, spinal cord injuries, Alzheimer's, Parkinson's, schizophrenia, and stroke among others. They are also used in both brain computer and brain machine interface systems with assistance, rehabilitative, and entertainment applications. Despite the broad interest in clinical applications, the use of brain signals has been only recently investigated by the scientific community as a biometric characteristic to be used in automatic people recognition systems. However, brain signals present some peculiarities, not shared by the most commonly used biometrics, such as face, iris, and fingerprints, with reference to privacy compliance, robustness against spoofing attacks, possibility to perform continuous identification, intrinsic liveness detection, and universality. These peculiarities make the use of brain signals appealing. On the other hand, there are many challenges which need to be properly addressed. The understanding of the level of uniqueness and permanence of brain responses, the design of elicitation protocols, and the invasiveness of the acquisition process are only few of the challenges which need to be tackled. In this paper, we further speculate on those issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications and intend to provide a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition, also reporting neurophysiological evidences related to the performed claims.
227 citations
"Biometric user authentication using..." refers background in this paper
...In paper [1], Patrizio Campisi and Daria La Rocca, elaborates that how much understanding level has been reached in using brain waves as a Biometric identifier and how much still to be achieved....
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...In paper [1], Patrizio Campisi and Daria La Rocca, reviewed some issues related to EEG as authentication, which forms an obstacle in the implementation of biometric systems...
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Book•
01 Jan 1999
64 citations
TL;DR: The present results confirm the existence of a sufficient degree of EEG reproducibility, at least un normal young women, to justify speaking of characteristic individual ‘baseline’ patterns during quiet wakefulness.
54 citations
"Biometric user authentication using..." refers background in this paper
...information about the brain conditions which, in the studies conducted so far, have shown to have some discriminant capabilities among subjects [9], [10], due to both morphological and anatomical traits, and functional plasticity traits....
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TL;DR: It could be shown that a subject can be characterized and be recognized by his EEG spectral pattern with high reliability and a confidence probability of almost 90%, implying a close relationship between the EEG spectral patterns and the individual person.
51 citations