Abstract
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.
Document Type
Article
Publication Date
2-3-2019
Digital Object Identifier (DOI)
https://doi.org/10.1155/2019/6862031
Related Content
The EEG data used to support the findings of this study are available from the corresponding author upon request.
Repository Citation
Abiri, Reza; Borhani, Soheil; Jiang, Yang; and Zhao, Xiaopeng, "Decoding Attentional State to Faces and Scenes Using EEG Brainwaves" (2019). Behavioral Science Faculty Publications. 50.
https://uknowledge.uky.edu/behavsci_facpub/50
Notes/Citation Information
Published in Complexity, v. 2019, article ID 6862031, p. 1-10.
Copyright © 2019 Reza Abiri et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.