Identifying individuals using EEG-based brain connectivity patterns

Hadri Hussain, Chee-Ming Ting, M. A. Jalil, Kanad Ray, S. Z.H. Rizvi, J. Kavikumar, Fuad M. Noman, A. L.Ahmad Zubaidi, Yin Fen Low, Sh-Hussain, Mufti Mahmud, M. Shamim Kaiser, J. Ali

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationBrain Informatics - 14th International Conference, BI 2021 Virtual Event, September 17–19, 2021 Proceedings
EditorsMufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
Place of PublicationCham Switzerland
PublisherSpringer
Pages124-135
Number of pages12
ISBN (Electronic)9783030869939
ISBN (Print)9783030869922
DOIs
Publication statusPublished - 2021
EventInternational Conference on Brain Informatics 2021 - Online, Padova, Italy
Duration: 17 Sept 202119 Sept 2021
Conference number: 14th
https://link.springer.com/book/10.1007/978-3-030-86993-9 (Proceedings)
https://www.bi2021.org/ (Website)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12960
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Brain Informatics 2021
Abbreviated titleBI 2021
Country/TerritoryItaly
CityPadova
Period17/09/2119/09/21
Internet address

Keywords

  • Coherence
  • Electrocardiogram
  • Electroencephalogram
  • Multivariate autoregressive
  • Partial directed coherence
  • Vector autoregressive

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