Brain waves for automatic biometric-based user recognition

Campisi, Patrizio; Rocca, Daria La

doi:10.1109/tifs.2014.2308640

Cited by 223 publications

(148 citation statements)

References 78 publications

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“…Among people, either during specific mental tasks or resting state, it is shown that specific features of the brain activity have different degrees of distinctiveness when dealing with EEG signals. Time and frequency are the two domains used mostly in which EEG features are extracted; most features of these domains rely on the resting state during the extraction process (Campisi and La Rocca, 2014). In addition, changes in blood cells such as oxyhemoglobin, deoxyhemoglobin and total hemoglobin are extracted as features for FNRIS (Serwadda et al, 2015).…”

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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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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“…loss of the iris), or burned fingers, etc. [2]. Recent studies have shown that the EEG signals have biometric possibility because the brain signals are distinctive and impossible to replicate and/or steal.…”

Section: Introductionmentioning

confidence: 99%

Brainwaves for User Verification using Two Separate Sets of Features based on DCT and Wavelet

George¹,

Hadi²

2018

ijacsa

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Abstract-This paper discusses the effectiveness of brain waves for user verification using electroencephalogram (EEG) recordings of one channel belong to single task. The feature sets were previously introduced as features for EEG-based identification system are tested as suitable features for verification system in this paper. The first considered feature set is based on the energy distribution of DCT's or DFT's power spectra, while the second set is based on the statistical moments of wavelet transform, three types of wavelet transforms is proposed. Each set of features is tested using normalized Euclidean distance measure for the matching purpose. The performance of the verification system is evaluated using FAR, FRR, and HTER measures. Two publicly available EEG datasets are used; first is the Colorado State University (CSU) dataset which was collected from seven healthy subjects and the second is the Motor Movement /Imagery (MMI) dataset which is a relatively large dataset was collected from 109 healthy subjects. The attained verification results are encouraging when compared with the results of other recent published works, the best achieved HTER is (0.26) when the system was tested on CSU dataset, while the best achieved HTER is (0.16) when the system was tested on MMI dataset for the features which based on the energy of DFT spectra.

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“…The signal acquisition session is then repeated over time to make the system more discriminative and robust to errors. In a recent paper, Campisi and La Rocca (2014) presented a review on the state-of-the-art of EEG-based automatic recognition systems, as well as an overview of the neurophysiological basis that constitutes the foundations on which EEG biometric systems can be built. The authors also discussed about the major obstacles towards the deployment of EEG based biometric systems in everyday life.…”

Section: Introductionmentioning

confidence: 99%

EEG-based person identification through Binary Flower Pollination Algorithm

Rodrigues

Silva

Papa

et al. 2016

Expert Systems with Applications

124

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Electroencephalogram (EEG) signal presents a great potential for highly secure biometric systems due to its characteristics of universality, uniqueness, and natural robustness to spoofing attacks. EEG signals are measured by sensors placed in various positions of a person's head (channels). In this work, we address the problem of reducing the number of required sensors while maintaining a comparable performance. We evaluated a binary version of the Flower Pollination Algorithm under different transfer functions to select the best subset of channels that maximizes the accuracy, which is measured by means of the Optimum-Path Forest classifier. The experimental results show the proposed approach can make use of less than a half of the number of sensors while maintaining recognition rates up to 87%, which is crucial towards the effective use of EEG in biometric applications.

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Brain waves for automatic biometric-based user recognition

Cited by 223 publications

References 78 publications

A Review Study of Brian Activity-Based Biometric Authentication

A Review Study of Brian Activity-Based Biometric Authentication

Brainwaves for User Verification using Two Separate Sets of Features based on DCT and Wavelet

EEG-based person identification through Binary Flower Pollination Algorithm

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