Using Portable EEG to Assess Human Visual Attention

Author(s):  
Olave E. Krigolson ◽  
Chad C. Williams ◽  
Francisco L. Colino
Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5178
Author(s):  
Sangbong Yoo ◽  
Seongmin Jeong ◽  
Seokyeon Kim ◽  
Yun Jang

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.


2013 ◽  
Vol 85 ◽  
pp. 5-19 ◽  
Author(s):  
Miguel P. Eckstein ◽  
Stephen C. Mack ◽  
Dorion B. Liston ◽  
Lisa Bogush ◽  
Randolf Menzel ◽  
...  

Author(s):  
Adhi Prahara ◽  
Murinto Murinto ◽  
Dewi Pramudi Ismi

The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity.


1997 ◽  
Vol 20 (4) ◽  
pp. 747-747 ◽  
Author(s):  
John M. Findlay ◽  
Valerie Brown ◽  
Iain D. Gilchrist

This commentary centres around the system of human visual attention. Although generally supportive of the position advocated in the target article, we suggest that the detailed account overestimates the capacities of active human vision. Limitations of peripheral search and saccadic accuracy are discussed in relation to the division of labour between covert and overt attentional processes.


2000 ◽  
Vol 4 (2) ◽  
pp. 57-61 ◽  
Author(s):  
Hiroaki Mizuhara ◽  
Jing-Long Wu ◽  
Yoshikazu Nishikawa

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