Identifying individuals using EEG-Based brain connectivity patterns
Considering the recent rapid advancements in digital technology, electroencephalogram (EEG) signal is a potential candidate for a robust human biometric authentication system. In this paper the focus of investigation is the use of brain activity as a new modality for identification. Univariate mod...
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Format: | Conference or Workshop Item |
Language: | English |
Published: |
2021
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Online Access: | http://eprints.unisza.edu.my/4147/1/FH03-FP-21-56622.pdf http://eprints.unisza.edu.my/4147/ |
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Summary: | Considering the recent rapid advancements in digital technology, electroencephalogram (EEG) signal is
a potential candidate for a robust human biometric authentication system. In this paper the focus of
investigation is the use of brain activity as a new modality for identification. Univariate model
biometrics such as speech, heart sound and electrocardiogram (ECG) require high-resolution computer
system with special devices. The heart sound is obtained by placing the digital stethoscope on the
chest, the ECG signals at the hands or chest of the client and speaks into a microphone for speaker
recognition. It is challenging task when adapting these technologies to human beings. This paper
proposed a series of tasks in a single paradigm rather than having users perform several tasks one by
one. The advantage of using brain electrical activity as suggested in this work is its uniqueness; the
recorded brain response cannot be duplicated, and a person’s identity is therefore unlikely to be forged
or stolen. The disadvantage of applying univariate is that the process only includes correlation in time
precedence of a signal, while the correlation between regions is ignored. The inter-regional could not
be assessed directly from univariate models. The alternative to this problem is the generalization of
univariate model to multivariate modeling, hypothesized that the inter-regional correlations could give
additional information to discriminate between brain conditions where the models or methods can
measure the synchronization between coupling regions and the coherency among them on brain
biometrics. The key issue is to handle the single task paradigm proposed in this paper with
multivariate signal EEG classification using Multivariate Autoregressive (MVAR) rather than univariate
model. The brain biometric systems obtained a significant result of 95.33% for dynamic Vector
autoregressive (VAR) time series and 94.59% for Partial Directed Coherence (PDC) and Coherence (COH)
frequency domain features. © 2021, Springer Nature Switzerland AG. |
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