BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs

Wang, Min; Hu, Jiankun; Abbass, Hussein A.

doi:10.1016/j.patcog.2020.107381

Cited by 85 publications

(62 citation statements)

References 34 publications

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“…ere are considerable individual differences in brain structure and extensive cognitive functions. Different individuals have unique connectivity between different functional areas of the brain [23,24]. e EEG signal of different individuals performing the same task is significantly different, but that of the same individual performing the same task is relatively stable and repeatable.…”

Section: Physiological Characteristics Of Eegmentioning

confidence: 99%

Review on EEG‐Based Authentication Technology

Zhang

Sun

Mao

et al. 2021

Computational Intelligence and Neuroscience

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With the rapid development of brain-computer interface technology, as a new biometric feature, EEG signal has been widely concerned in recent years. The safety of brain-computer interface and the long-term insecurity of biometric authentication have a new solution. This review analyzes the biometrics of EEG signals, and the latest research is involved in the authentication process. This review mainly introduced the method of EEG-based authentication and systematically introduced EEG-based biometric cryptosystems for authentication for the first time. In cryptography, the key is the core basis of authentication in the cryptographic system, and cryptographic technology can effectively improve the security of biometric authentication and protect biometrics. The revocability of EEG-based biometric cryptosystems is an advantage that traditional biometric authentication does not have. Finally, the existing problems and future development directions of identity authentication technology based on EEG signals are proposed, providing a reference for the related studies.

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Section: Physiological Characteristics Of Eegmentioning

confidence: 99%

Review on EEG‐Based Authentication Technology

Zhang

Sun

Mao

et al. 2021

Computational Intelligence and Neuroscience

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“…In [26], the author used the spectral decomposition of a graph defined by a geometrical distribution of electrodes to achieve EEG signal dimensionality reduction. In [27], a graph-based method for EEG biometric identification was proposed, which consisted of a network estimation module and a graph analysis module.…”

Section: Introductionmentioning

confidence: 99%

A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface

Jin

Sun

Daly

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph.We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.

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“…Neural activities of the brain are shown in the form of EEG signals which are captured with the help of multiple-electrode EEG machines either over the cortex under the skull (inside the brain) or over the scalp [1]. The representation of EEG signal is done in the time domain whereas, few EEG measuring devices are available which can perform some signal processing steps to obtain frequency analysis along with imaging tools to visualize EEG topographies [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

Narayan¹

2021

TURCOMAT

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Electroencephalogram (EEG)signals based brain-computer interfacing (BCI) is the current technology trends in the field of rehabilitation robotic. This study compared the performance of support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) classifier with the combination of eight different features as a feature vector. EEG data were acquired from 20 healthy human subjects with predefined protocols. After the EEG signals acquisition, it was pre-processed followed by feature extraction and classification by using SVM MLP and LDA classifiers. The results exhibited that the SVM method was the best approach with 98.8% classification accuracy followed by MLP classifier. Finally, the SVM classifier and Arduino Mega controller was employed for offline controlling of the gripper of the robotic arm prototype. The finding of this study may be useful for online controlling as well as multi-degree of freedom with multi-class EEG dataset.

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BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs

Cited by 85 publications

References 34 publications

Review on EEG‐Based Authentication Technology

Review on EEG‐Based Authentication Technology

A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface

Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

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