Survey of EEG-based biometric authentication

Jayarathne, Isuru; Cohen, Michael; Amarakeerthi, Senaka

doi:10.1109/icawst.2017.8256471

Cited by 30 publications

(14 citation statements)

References 14 publications

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“…Pozo-Banos et al [5] reported in a comprehensive review about EEG subject identification that EEG has subject-specific information. Jayarathne et al [6] and Almehmadi et al [7] compiled comprehensive surveys of EEG-based access control systems. These surveys reported suitability, state of the art of EEG for person identification, and important parameters which must be adjusted to implement an authentication system with desired security and usability levels.…”

Section: Related Researchmentioning

confidence: 99%

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio

2020

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Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person identification using EEG is an attraction among researchers, the complexity of sensing limits using such technologies in real-world applications. In this research, the challenge has been addressed by reducing the complexity of the brain signal acquisition and analysis processes. This was achieved by reducing the number of electrodes, simplifying the critical task without compromising accuracy. Event-related potentials (ERP), a.k.a. time-locked stimulation, was used to collect data from each subject's head. Following a relaxation period, each subject was visually presented a random four-digit number and then asked to think of it for 10 seconds. Fifteen trials were conducted with each subject with relaxation and visual stimulation phases preceding each mental recall segment. We introduce a novel derived feature, dubbed Inter-Hemispheric Amplitude Ratio (IHAR), which expresses the ratio of amplitudes of laterally corresponding electrode pairs. The feature was extracted after expanding the training set using signal augmentation techniques and tested with several machine learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). Most of the ML algorithms showed 100% accuracy with 14 electrodes, and according to our results, perfect accuracy can also be achieved using fewer electrodes. However, AF3, AF4, F7, and F8 electrode combination with kNN classifier which yielded 99.0±0.8% testing accuracy is the best for person identification to maintain both user-friendliness and performance. Surprisingly, the relaxation phase manifested the highest accuracy of the three phases.

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Section: Related Researchmentioning

confidence: 99%

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio

2020

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“…The authors are going to review a couple of different tactics of EEG capturing methods to acquire better accuracy and check the applicability of using signal authentication purposes. There are four different studies in this research area that were more successful and reported better accuracy in their experiences (Jayarathne et al 2017) which are linear discriminant analysis (LDA), cosine similarity → LDA, power spectral density (PSD) and spectral coherence (COH) → Mahalanobis distance and matchscore fusion, and event-related potentials (ERP). These studies used different tasks, extracted features, and classifiers for doing their experiments to get higher accuracy rates of brainwaves to use them for authentication purposes.…”

Section: Bci Security Authentication Using Eeg Signalsmentioning

confidence: 99%

“…Summary of various studies (in decreasing order of accuracy)(Jayarathne et al 2017) DiscussionRecent biometric user authentication techniques have some problems and limitations. To cover the recent biometric limitations, we need a new biometric brainwave-based authentication, which is another technique in the extensive range of authentication systems.…”

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confidence: 99%

Brain Signals as a New Biometric Authentication Method Using Brain-Computer Interface

Kolivand

2019

Encyclopedia of Computer Graphics and Games

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Biometric authentication; Brain-computer interface; EEG signal DefinitionsHuman biometric techniques are presented as another type of security authentication to cover the problems of password authentication. Brainwave is another human biometric, which recently is one of the popular subjects for scientists and researchers. Brain-computer interface (BCI) is a method of communication based on neural activity's communication created by the brain.

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“…The analysis of EEG is more consistent as the signals recorded are more substantial and explicit [11]. Besides, EEG is universal, unique, and robust, making it suitable to be used as cognitive measures for biometric identification [12].…”

Section: Introductionmentioning

confidence: 99%

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification

Lai

Ibrahim

Abdullah

et al. 2019

Computational Intelligence and Neuroscience

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Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.

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Survey of EEG-based biometric authentication

Cited by 30 publications

References 14 publications

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio

Brain Signals as a New Biometric Authentication Method Using Brain-Computer Interface

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification

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