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Decoding Attentional State to Faces and Scenes Using EEG Brainwaves
Joint Authors
Zhao, Xiaopeng
Abiri, Reza
Borhani, Soheil
Jiang, Yang
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Attention is the ability to facilitate processing perceptually salient information while blocking the irrelevant information to an ongoing task.
For example, visual attention is a complex phenomenon of searching for a target while filtering out competing stimuli.
In the present study, we developed a new Brain-Computer Interface (BCI) platform to decode brainwave patterns during sustained attention in a participant.
Scalp electroencephalography (EEG) signals using a wireless headset were collected in real time during a visual attention task.
In our experimental protocol, we primed participants to discriminate a sequence of composite images.
Each image was a fair superimposition of a scene and a face image.
The participants were asked to respond to the intended subcategory (e.g., indoor scenes) while withholding their responses for the irrelevant subcategories (e.g., outdoor scenes).
We developed an individualized model using machine learning techniques to decode attentional state of the participant based on their brainwaves.
Our model revealed the instantaneous attention towards face and scene categories.
We conducted the experiment with six volunteer participants.
The average decoding accuracy of our model was about 77%, which was comparable with a former study using functional magnetic resonance imaging (fMRI).
The present work was an attempt to reveal momentary level of sustained attention using EEG signals.
The platform may have potential applications in visual attention evaluation and closed-loop brainwave regulation in future.
American Psychological Association (APA)
Abiri, Reza& Borhani, Soheil& Jiang, Yang& Zhao, Xiaopeng. 2019. Decoding Attentional State to Faces and Scenes Using EEG Brainwaves. Complexity،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1132556
Modern Language Association (MLA)
Abiri, Reza…[et al.]. Decoding Attentional State to Faces and Scenes Using EEG Brainwaves. Complexity No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1132556
American Medical Association (AMA)
Abiri, Reza& Borhani, Soheil& Jiang, Yang& Zhao, Xiaopeng. Decoding Attentional State to Faces and Scenes Using EEG Brainwaves. Complexity. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1132556
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132556