A study on visual attention modeling - A linear regression method based on EEG

Dong, Q. and Hu, B. and Zhang, J. and Li, X. and Ratcliffe, M. (2013) A study on visual attention modeling - A linear regression method based on EEG. In: 2013 International Joint Conference on Neural Networks, IJCNN 2013, 4 August 2013 through 9 August 2013, Dallas, TX; United States.

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Abstract

In an increasingly knowledge based world, people are confronted with an explosion of information from the environment which must be viewed in restricted attention spans. Hence there is a need to investigate how best to model our Visual Attention (VA) with a view to allocate our attention efficiently. We use the color-word Stroop task combined with electroencephalogram (EEG) to model VA: subjects undertake the Stroop task and their EEG is recorded. This is in contrast to other studies that use techniques such as Event Related Potentials (ERP), Contextual Modeling Frameworks, eye movements and facial recognition. The paper presents a simple and useful model to recognize VA dynamically. We use the linear EEG features of different cortical fields as the main inference factors, and take the response time (RT) of the Stroop task as a metric to quantify subject performance. First, we obtain the most relevant EEG feature vectors from the recording, using a correlation analysis. Second, we use experimental data for training the VA model, using a regression method. Last, we then apply further experimental data to test the proposed model. The results from the tests conducted demonstrate that our model maps visual attention very closely. © 2013 IEEE.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Correlation Analysis, EEG, Linear Regression, Stroop task, Visual Attention (VA), Contextual modeling, Correlation analysis, Electro-encephalogram (EEG), Event-related potentials, Linear regression methods, Stroop task, Visual Attention, Visual attention model, Behavioral research, Correlation methods, Eye movements, Face recognition, Knowledge based systems, Linear regression, Neural networks, Electroencephalography
Subjects: G500 Information Systems
Divisions: UoA Collections > UoA11: Computer Science and Informatics
Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Hussen Farooq
Date Deposited: 27 Jul 2016 12:19
Last Modified: 27 Jul 2016 12:19
URI: http://www.open-access.bcu.ac.uk/id/eprint/2559

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