Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals

Chan, Hui-Ling; Chen, Lifen; Chen, I-Tzu; Chen, Yongsheng

doi:10.1016/j.neuroimage.2015.03.038

Cited by 5 publications

(9 citation statements)

References 108 publications

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“…There were still many assignments which survived from the adjustment using the threshold level of 5%. There were 10 edges between clusters 1 and 2, 1 edge between clusters 1 and 3, 2 edges for each of cluster pairs (1,4), (2,3), (2,4) and (3,4). We also made Hochberg's adjustment on these pvalues by using a stringent threshold level, say 1%, obtaining the edge links for the following six pairs of nodes (v 31 , v 15 ), (v 17 , v 21 ), (v 27 , v 30 ), (v 2 , v 25 ), (v 6 , v 18 ) and (v 23 , v 24 ).…”

Section: Face-perception Datamentioning

confidence: 99%

Depth-invariant beamforming for functional connectivity with MEG data

Zhang

2022

Statistics and Its Interface

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Section: Face-perception Datamentioning

confidence: 99%

Depth-invariant beamforming for functional connectivity with MEG data

Zhang

2022

Statistics and Its Interface

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“…Medication may cause the power increase of beta and theta bands (Blume, 2006 ). Negative and positive emotional states induce different patterns of functional connectivity (Chan et al, 2012 , 2015b ; Lee and Hsieh, 2014 ). Patients suffering from Parkinson’s disease exhibit slower EEG in theta band than that of the normal controls (Soikkeli et al, 1991 ).…”

Section: Future Directionsmentioning

confidence: 99%

“…The second approach is the use of connectivity in the human brain, including functional and effective connectivity (Friston, 2011 ), to assess the interactions between various regions of the brain. Functional connectivity measures the statistical dependence between activity in spatially segregated regions using linear indices, such as correlation (Chan et al, 2015b ), and non-linear indices, such as phase locking values (Tass et al, 1998 ) and phase-amplitude coupling (Tort et al, 2010 ; Chan et al, 2015a ). Effective connectivity quantifies the causal effects among various neural systems using approaches such as dynamic causal modeling (Kiebel et al, 2009 ) and Granger causal modeling (David et al, 2006 ).…”

Section: Future Directionsmentioning

confidence: 99%

Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition

Chan

Kuo

Cheng

et al. 2018

Front. Neuroinform.

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The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.

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“…Other factors such as medications may cause a power increase in both beta and theta bands resulting in results falsification ( Blume, 2006 ). Emotional states may also induce different functional connectivity patterns in brain waves ( Chan et al, 2015 ). It was also found that patients suffering from AD exhibit lower EEG, especially in the theta band, than normal controls ( Lerch et al, 2005 ; Radić et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Brain-printing biometrics underlying mechanism as an early diagnostic technique for Alzheimer's disease neurodegenerative type

Anwar

2021

Current Research in Physiology

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Early diagnosis of neurodegenerative diseases, especially Alzheimer's disease (AD), is essential for implementing the appropriate treatment protocols and controlling disease progression. Early AD diagnosis helps patients achieve the best therapeutic outcomes, lessening irreversible neurodegenerative damage and severe cognitive decline. The measurement of brain waves and structural modifications, including gray/white matter and brain volume, have recently been considered a promising approach for brain biometrics because of the inherent specificity, degree of confidentiality, and reproducibility. Brain printing biometrics (BPB) is thus becoming more commonly considered as tool for early AD detection. This review proposes using BPB as a tool for the detection of AD prior to the appearance of persistent hallmark depositions, including Aβ and tau protein aggregations in different brain regions. It also describes BPB authentication, a method of implementation, as well as potential outcomes.

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Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals

Cited by 5 publications

References 108 publications

Depth-invariant beamforming for functional connectivity with MEG data

Depth-invariant beamforming for functional connectivity with MEG data

Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition

Brain-printing biometrics underlying mechanism as an early diagnostic technique for Alzheimer's disease neurodegenerative type

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