Person authentication based on eye-closed and visual stimulation using EEG signals

Yap, Hui Yen; Choo, Yun-Huoy; Yusoh, Zeratul Izzah Mohd; Khoh, Wee How

doi:10.1186/s40708-021-00142-4

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Yap et al [29] investigated the potential of developing an EEG biometric system using a self-collected database of eight participants and an SVM classifier. The subjects were asked to perform two tasks: resting state with eyes closed, and visual stimulation.…”

Section: Literature Reviewmentioning

confidence: 99%

Cryptographic Algorithm Designed by Extracting Brainwave Patterns

Dragu,

Nicolae,

Frunzete

2024

Mathematics

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A new authentication method based on EEG signal is proposed here. Biometric features such as fingerprint scanning, facial recognition, iris scanning, voice recognition, and even brainwave patterns can be used for authentication methods. Brainwave patterns, also known as brain biometrics, can be captured using technologies like electroencephalography (EEG) to authenticate a user based on their unique brain activity. This method is still in the research phase and is not yet commonly used for authentication purposes. Extracting EEG features for authentication typically involves signal processing techniques to analyze the brainwave patterns. Here, a method based on statistics for extracting EEG features is designed to extract meaningful information and patterns from the brainwave data for various applications, including authentication, brain–computer interface systems, and neurofeedback training.

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Section: Literature Reviewmentioning

confidence: 99%

Cryptographic Algorithm Designed by Extracting Brainwave Patterns

Dragu,

Nicolae,

Frunzete

2024

Mathematics

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“…Studies relating to our proposed model are not common because most studies focus on identifying other biological signals, such as the iris and fingerprints [69,70], rather than brain waves. Many researchers also focused on classifying the brain signals recorded between cognitive tasks and non-tasks as performed by a user [61,71]. Moreover, previous studies have provided relatively low accuracy: Andreas M. Ray et al previously reported that the use of 27 healthy subjects achieved a mean classification accuracy of 75.30% [72].…”

Section: B Difficulty Classifying the One User From The Non-usersmentioning

confidence: 99%

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

Bak

Jeong

2023

IEEE Access

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Human brain activities-electroencephalogram (EEG) signals-are likely to provide a secure biometric approach for user identification because they are more sensitive, secretive, and difficult to replicate. Many studies have recently focused on identifying and quantifying important frequency patterns in motor imagery (MI), recorded through EEG. However, there is still a lack of an optimal methodology for recognizing users with EEG-based MI. Therefore, we aimed to propose an EEG-MI methodology that utilizes optimized feature extraction methods and classifiers to improve user-aware accuracy. To accomplish this goal, we extracted four features related to MI and compared the accuracies for recognizing users using a support vector machine (SVM) and Gaussian Naïve Bayes (GNB). We then used the half-total error rate (HTER) to determine whether the results were reliable due to an imbalance problem caused by the differences in the data sizes. Thus, we used a common spatial pattern (CSP) to achieve the highest user identification accuracies of 98.97% and 97.47% using SVM and GNB, respectively. All user recognition accuracies are guaranteed by the HTERs, which are below 0.5. However, CSP has the disadvantage of decreasing accuracy on a small dataset scale. Therefore, we proposed and tested a statistical methodology for estimating a minimum dataset scale to ensure CSP performance. We confirm that the used dataset adequately guarantees CSP performance. This study makes a great contribution to the field of information security by presenting an EEG-MI methodology that improves the identification accuracy in human biometrics based on EEG-MI signals.

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“…Yap H. Y. et al [ 42 ] dealt with biometry based on two EEG acquisition protocols, namely eyes-closed and visual stimulation by words displayed on an LED screen. They created their own database of eight subjects.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Person Identification during Escalating Cognitive Load

Kralikova

Babušiak

Šmondrk

2022

Sensors

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With the development of human society, there is an increasing importance for reliable person identification and authentication to protect a person’s material and intellectual property. Person identification based on brain signals has captured substantial attention in recent years. These signals are characterized by original patterns for a specific person and are capable of providing security and privacy of an individual in biometric identification. This study presents a biometric identification method based on a novel paradigm with accrual cognitive brain load from relaxing with eyes closed to the end of a serious game, which includes three levels with increasing difficulty. The used database contains EEG data from 21 different subjects. Specific patterns of EEG signals are recognized in the time domain and classified using a 1D Convolutional Neural Network proposed in the MATLAB environment. The ability of person identification based on individual tasks corresponding to a given degree of load and their fusion are examined by 5-fold cross-validation. Final accuracies of more than 99% and 98% were achieved for individual tasks and task fusion, respectively. The reduction of EEG channels is also investigated. The results imply that this approach is suitable to real applications.

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Person authentication based on eye-closed and visual stimulation using EEG signals

Cited by 9 publications

References 25 publications

Cryptographic Algorithm Designed by Extracting Brainwave Patterns

Cryptographic Algorithm Designed by Extracting Brainwave Patterns

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

EEG-Based Person Identification during Escalating Cognitive Load

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