Brain connectivity evaluation during selective attention using EEG-based brain-computer interface

Soheil Borhani; Reza Abiri; Yang Jiang; Taylor Berger; Xiaopeng Zhao

doi:10.1080/2326263X.2019.1651186

Brain connectivity evaluation during selective attention using EEG-based brain-computer interface

Soheil Borhani, Reza Abiri, Yang Jiang, Taylor Berger, Xiaopeng Zhao

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

Attentional deficits may be caused by neurological diseases, including Attention-Deficit/Hyperactivity Disorder (ADHD), Alzheimer’s disease (AD), Traumatic Brain Injuries (TBI), etc. This work aims to evaluate selective attention under visual stimulations of faces and scenes using electroencephalogram (EEG)-based brain-computer interface.The experiment consisted of two phases: 1) image recognition and 2) attention evaluation. In phase 1, the mean response time was 547 ms vs. 633 ms to faces and scenes, respectively. In phase 2, the mean response time was 667 ms vs. 706 ms to face and scene categories, respectively. We analyzed the event-related time-frequency representation of faces and scenes and the causal relationship between object recognition and the motor response associated with category selection using the brain connectivity based on Granger causality. The developed experimental protocols and connectivity evaluation methods may provide insights for a better understanding of the neural processes for object recognition and category selection.

Original language	English
Pages (from-to)	25-35
Number of pages	11
Journal	Brain-Computer Interfaces
Volume	6
Issue number	1-2
DOIs	https://doi.org/10.1080/2326263X.2019.1651186
State	Published - 2019

Bibliographical note

Funding Information:
This work was in part supported by Alzheimer's Tennessee, by the National Institutes of Health under grant numbers 5P30AG028383 and UL1TR000117, and by the National Science Foundation through Research Experience for Undergraduates (REU) award no. 1659502. We greatly thank Makoto Miyakoshi for his helpful comments on the preprocessing pipeline.

Publisher Copyright:
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

Brain-computer interface
EEG
Granger Causality
brain connectivity
event-related potential
selective attention
time-frequency

ASJC Scopus subject areas

Biomedical Engineering
Human-Computer Interaction
Behavioral Neuroscience
Electrical and Electronic Engineering

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10.1080/2326263X.2019.1651186

Cite this

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title = "Brain connectivity evaluation during selective attention using EEG-based brain-computer interface",

abstract = "Attentional deficits may be caused by neurological diseases, including Attention-Deficit/Hyperactivity Disorder (ADHD), Alzheimer{\textquoteright}s disease (AD), Traumatic Brain Injuries (TBI), etc. This work aims to evaluate selective attention under visual stimulations of faces and scenes using electroencephalogram (EEG)-based brain-computer interface.The experiment consisted of two phases: 1) image recognition and 2) attention evaluation. In phase 1, the mean response time was 547 ms vs. 633 ms to faces and scenes, respectively. In phase 2, the mean response time was 667 ms vs. 706 ms to face and scene categories, respectively. We analyzed the event-related time-frequency representation of faces and scenes and the causal relationship between object recognition and the motor response associated with category selection using the brain connectivity based on Granger causality. The developed experimental protocols and connectivity evaluation methods may provide insights for a better understanding of the neural processes for object recognition and category selection.",

keywords = "Brain-computer interface, EEG, Granger Causality, brain connectivity, event-related potential, selective attention, time-frequency",

author = "Soheil Borhani and Reza Abiri and Yang Jiang and Taylor Berger and Xiaopeng Zhao",

note = "Funding Information: This work was in part supported by Alzheimer's Tennessee, by the National Institutes of Health under grant numbers 5P30AG028383 and UL1TR000117, and by the National Science Foundation through Research Experience for Undergraduates (REU) award no. 1659502. We greatly thank Makoto Miyakoshi for his helpful comments on the preprocessing pipeline. Publisher Copyright: {\textcopyright} 2019, {\textcopyright} 2019 Informa UK Limited, trading as Taylor & Francis Group.",

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AU - Abiri, Reza

AU - Jiang, Yang

AU - Berger, Taylor

AU - Zhao, Xiaopeng

N1 - Funding Information: This work was in part supported by Alzheimer's Tennessee, by the National Institutes of Health under grant numbers 5P30AG028383 and UL1TR000117, and by the National Science Foundation through Research Experience for Undergraduates (REU) award no. 1659502. We greatly thank Makoto Miyakoshi for his helpful comments on the preprocessing pipeline. Publisher Copyright: © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

PY - 2019

Y1 - 2019

N2 - Attentional deficits may be caused by neurological diseases, including Attention-Deficit/Hyperactivity Disorder (ADHD), Alzheimer’s disease (AD), Traumatic Brain Injuries (TBI), etc. This work aims to evaluate selective attention under visual stimulations of faces and scenes using electroencephalogram (EEG)-based brain-computer interface.The experiment consisted of two phases: 1) image recognition and 2) attention evaluation. In phase 1, the mean response time was 547 ms vs. 633 ms to faces and scenes, respectively. In phase 2, the mean response time was 667 ms vs. 706 ms to face and scene categories, respectively. We analyzed the event-related time-frequency representation of faces and scenes and the causal relationship between object recognition and the motor response associated with category selection using the brain connectivity based on Granger causality. The developed experimental protocols and connectivity evaluation methods may provide insights for a better understanding of the neural processes for object recognition and category selection.

AB - Attentional deficits may be caused by neurological diseases, including Attention-Deficit/Hyperactivity Disorder (ADHD), Alzheimer’s disease (AD), Traumatic Brain Injuries (TBI), etc. This work aims to evaluate selective attention under visual stimulations of faces and scenes using electroencephalogram (EEG)-based brain-computer interface.The experiment consisted of two phases: 1) image recognition and 2) attention evaluation. In phase 1, the mean response time was 547 ms vs. 633 ms to faces and scenes, respectively. In phase 2, the mean response time was 667 ms vs. 706 ms to face and scene categories, respectively. We analyzed the event-related time-frequency representation of faces and scenes and the causal relationship between object recognition and the motor response associated with category selection using the brain connectivity based on Granger causality. The developed experimental protocols and connectivity evaluation methods may provide insights for a better understanding of the neural processes for object recognition and category selection.

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KW - event-related potential

KW - selective attention

KW - time-frequency

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Brain connectivity evaluation during selective attention using EEG-based brain-computer interface

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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