scholarly journals EyeMMV toolbox: An eye movement post-analysis tool based on a two-step spatial dispersion threshold for fixation identification

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Vassilios Krassanakis ◽  
Vassiliki Filippakopoulou ◽  
Byron Nakos
Keyword(s):  
Eye Movement ◽  
Spatial Dispersion ◽  
Computer Software ◽  
Movement Analysis ◽  
Source Analysis ◽  
Analysis Tool ◽  
Identification Algorithm ◽  
Strongly Correlated ◽  
Scripting Language ◽  
Visualization Techniques

Eye movement recordings and their analysis constitute an effective way to examine visual perception. There is a special need for the design of computer software for the performance of data analysis. The present study describes the development of a new toolbox, called EyeMMV (Eye Movements Metrics & Visualizations), for post experimental eye movement analysis. The detection of fixation events is performed with the use of an introduced algorithm based on a two-step spatial dispersion threshold. Furthermore, EyeMMV is designed to support all well-known eye tracking metrics and visualization techniques. The results of fixation identification algorithm are compared with those of an algorithm of dispersion-type with a moving window, imported in another open source analysis tool. The comparison produces outputs that are strongly correlated. The EyeMMV software is developed using the scripting language of MATLAB and the source code is distributed through GitHub under the third version of GNU General Public License (link: https://github.com/krasvas/EyeMMV).

Adaptive reasoning: Componential and eye movement analysis of geometric analogy performance

Intelligence ◽  
1984 ◽  
Vol 8 (3) ◽  
pp. 205-238 ◽  
Author(s):  
Charles E. Bethell-Fox ◽  
David F. Lohman ◽  
Richard E. Snow
Keyword(s):  
Eye Movement ◽  
Movement Analysis ◽  
Eye Movement Analysis

scholarly journals Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

2021 ◽  
Vol 12 (1) ◽  
pp. 49-64
Author(s):  
Gavindya Jayawardena ◽  
Sampath Jayarathna
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Eye Gaze ◽  
Movement Analysis ◽  
Movement Data ◽  
Bounding Boxes ◽  
Multiple Granularity ◽  
Instance Segmentation ◽  
Segmentation Models

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements & object instance segmentation models capture 30% of eye movements.

scholarly journals 1D3-4　Eye Movement Analysis of Expert on the Backing Process of Scrolls on the Backing process of Scrolls

2013 ◽  
Vol 49 (Supplement) ◽  
pp. S182-S183
Author(s):  
Yasuo OKA ◽  
Iwataro OKA ◽  
Chieko NARITA ◽  
Yuka TAKAI ◽  
Akihiko GOTO ◽  
...  
Keyword(s):  
Eye Movement ◽  
Movement Analysis ◽  
Eye Movement Analysis
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