Decoding Attentional State to Faces and Scenes Using EEG Brainwaves
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...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/6862031 |
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| author | Reza Abiri Soheil Borhani Yang Jiang Xiaopeng Zhao |
| author_facet | Reza Abiri Soheil Borhani Yang Jiang Xiaopeng Zhao |
| author_sort | Reza Abiri |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2fea9e243f5547508f2ba691d159db0f |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2fea9e243f5547508f2ba691d159db0f2025-08-20T03:20:22ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/68620316862031Decoding Attentional State to Faces and Scenes Using EEG BrainwavesReza Abiri0Soheil Borhani1Yang Jiang2Xiaopeng Zhao3Department of Neurology, University of California, San Francisco/Berkeley, CA 94158, USADepartment of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USADepartment of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY 40356, USADepartment of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USAAttention 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.http://dx.doi.org/10.1155/2019/6862031 |
| spellingShingle | Reza Abiri Soheil Borhani Yang Jiang Xiaopeng Zhao Decoding Attentional State to Faces and Scenes Using EEG Brainwaves Complexity |
| title | Decoding Attentional State to Faces and Scenes Using EEG Brainwaves |
| title_full | Decoding Attentional State to Faces and Scenes Using EEG Brainwaves |
| title_fullStr | Decoding Attentional State to Faces and Scenes Using EEG Brainwaves |
| title_full_unstemmed | Decoding Attentional State to Faces and Scenes Using EEG Brainwaves |
| title_short | Decoding Attentional State to Faces and Scenes Using EEG Brainwaves |
| title_sort | decoding attentional state to faces and scenes using eeg brainwaves |
| url | http://dx.doi.org/10.1155/2019/6862031 |
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