Low-cost electroencephalogram (EEG) based authentication

Ashby, Corey; Bhatia, Anupam; Tenore, Francesco V.; Vogelstein, R. Jacob

doi:10.1109/ner.2011.5910581

Cited by 108 publications

(74 citation statements)

References 8 publications

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“…We are able to maintain a high level of authentication accuracy with a subject pool that is 66% to 275% larger than those from previous studies [3,12,[14][15][16], thus demonstrating the feasibility of authentication in a small population, e.g., a work group setting [14]. Nonetheless, it would still be valuable to investigate the scalability of the results to even larger populations.…”

Section: Usabilitymentioning

confidence: 71%

“…In each of these studies, the EEG data are captured using clinical-grade multi-channel sensors. More recently, Ashby et al achieved 100% authentication accuracy with 5 subjects using consumer-grade multi-channel sensors [3]. In each of these studies, all the subjects performed identical tasks, ranging from baseline relaxation to imaginary motor movement, visualization, and solving math problems.…”

Section: Brainwave-based Authenticationmentioning

confidence: 99%

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I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

Chuang

Nguyen

Wang

et al. 2013

Lecture Notes in Computer Science

130

115

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Abstract. With the embedding of EEG (electro-encephalography) sensors in wireless headsets and other consumer electronics, authenticating users based on their brainwave signals has become a realistic possibility. We undertake an experimental study of the usability and performance of user authentication using consumer-grade EEG sensor technology. By choosing custom tasks and custom acceptance thresholds for each subject, we can achieve 99% authentication accuracy using single-channel EEG signals, which is on par with previous research employing multichannel EEG signals using clinical-grade devices. In addition to the usability improvement offered by the single-channel dry-contact EEG sensor, we also study the usability of different classes of mental tasks. We find that subjects have little difficulty recalling chosen "pass-thoughts" (e.g., their previously selected song to sing in their mind). They also have different preferences for tasks based on the perceived difficulty and enjoyability of the tasks. These results can inform the design of authentication systems that guide users in choosing tasks that are both usable and secure.

show abstract

Section: Usabilitymentioning

confidence: 71%

Section: Brainwave-based Authenticationmentioning

confidence: 99%

I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

Chuang

Nguyen

Wang

et al. 2013

Lecture Notes in Computer Science

130

115

View full text Add to dashboard Cite

show abstract

“…A front-end part set on a cell phone in charge of client communication and a back-end part set on a remote server in charge of preparing EEG information and taking care of the validation calculations [49]. Short EEG recordings can be changed to speak to one of a kind bio-metric identifiers, including both: behavioral and physiological qualities.…”

Section: Brain Wave Based Authenticationmentioning

confidence: 99%

Modern Authentication Techniques in Smart Phones: Security and Usability Perspective

Shafique¹,

Khan²,

Waqar³

et al. 2017

ijacsa

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Abstract-A smartphone has more advanced computing ability and connectivity than basic featured phones. Presently, we are moving from the Internet society to a mobile society where more and more access to the information is required. This has resulted in a mobile security which is no longer immanent, but imperative. Smartphone authentication has received substantial attention of the research community for the past several years because there have been modern developments beyond the classical PINs and passwords making user authentication more challenging. In this paper, we critically analyze the attacks and the vulnerabilities in smartphones' authentication mechanisms. A comparative analysis of different authentication techniques along with the usage of the different authentication methods is discussed which lead the end-user towards choosing the most suitable and customizable authentication technique.

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“…The plotting time (in case it is required) was also taken into account. [25] considered the use as of a set of features with classification. After obtaining all this features, LDA or Linear SVM is used to classify the obtained signals for multiple classes.…”

Section: B Execution Time Performancementioning

confidence: 99%

An Online Synchronous Brain Wave Signal Pattern Classifier with Parallel Processing Optimization for Embedded System Implementation

Senzio-Savino¹,

Alsharif²,

Gutierrez³

et al. 2017

ijacsa

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Abstract-Commercial Brain Computer Interface applications are currently expanding due to the success of widespread dissemination of low cost devices. Reducing the cost of a traditional system requires appropriate resources, such as proper software tools for signal processing and characterization. In this paper, a methodology for classifying a set of attention and meditation brain wave signal patterns is presented by means of unsupervised signal feature clustering with batch Self-Organizing Maps (b-SOM) and supervised classification by Support Vector Machine (SVM). Previous research on this matter did not combine both methods and also required an important amount of computation time. With the use of a small square neuron grid by b-SOM and an RBF kernel SVM, a well delimited classifier was obtained. The recognition rate was 70% after parameter tuning. In terms of optimization, the parallel b-SOM algorithm reduced drastically the computation time, allowing online clustering and classification for full length input data.

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Low-cost electroencephalogram (EEG) based authentication

Cited by 108 publications

References 8 publications

I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

Modern Authentication Techniques in Smart Phones: Security and Usability Perspective

An Online Synchronous Brain Wave Signal Pattern Classifier with Parallel Processing Optimization for Embedded System Implementation

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