Eye tracking: From eye movements recordings to the analysis metrics and visualizations Vassilios Krassanakis
Dr. Engineer NTUA Dipl. Rural & Surveying Engineer NTUA
[email protected] https://sites.google.com/site/vassilioskrassanakis/
National and Kapodistrian University of Athens 7 March 2016 Dr. Vassilios Krassanakis, 7 March 2016
1
Contents ¡ Eye
tracking systems
¡ Eye
movements recordings
¡ Fixation
identification algorithms
¡ Data
quality check processes
¡ Main
and derived metrics
¡ Visualization ¡ Analysis
software
¡ Research Dr. Vassilios Krassanakis, 7 March 2016
techniques
studies examples 2
Eye tracking The methodology of eye movements monitoring. Eye tracking techniques measure: ¡ the
position of the eye relative to the head.
¡ the
orientation of the eye in space (“point of regard”).
(Duchowski, 2007; Young & Sheena, 1975) Dr. Vassilios Krassanakis, 7 March 2016
3
Eye tracking techniques ¡ Electro-OculoGraphy ¡ Scleral
(EOG)
contact lens – search coil
¡ Photo-Oculography
Oculography (VOG)
(POG) & Video-
¡ Video-Based
techniques based on pupil and corneal reflection.
Dr. Vassilios Krassanakis, 7 March 2016
4
Electro-OculoGraphy (EOG)
(Source: http://www.cesti.gov.vn/images/cesti/ stinfo/Nam%202013/So7/0713_SNTT_Eye %20tracking_nhien%20_H07.jpg) Dr. Vassilios Krassanakis, 7 March 2016
5
Scleral contact lens – search coil
(Duchowski, 2007 - Pictures: Skalar Medical, Delft, Netherlands) Dr. Vassilios Krassanakis, 7 March 2016
6
Video-Oculography (VOG)
(Strupp, Kremmyda, Adamczyk, Böttcher, Muth, Yip, & Bremova, 2014) Dr. Vassilios Krassanakis, 7 March 2016
7
Pupil & corneal reflection
(Source: http://apps.usd.edu/coglab/schieber/eyetracking/ets/ets-calibration.html) Dr. Vassilios Krassanakis, 7 March 2016
8
Pupil & corneal reflection detection
(Source: http://users.ntua.gr/bnakos/Eye_Tracking_Eng.html) Dr. Vassilios Krassanakis, 7 March 2016
9
Eye tracker system’s operation: A prototype diagram
(Source: http://users.ntua.gr/bnakos/Eye_Tracking_Eng.html)
Dr. Vassilios Krassanakis, 7 March 2016
10
Head mounted eye tracking device
(Source: http://www.asleyetracking.com/Site/Portals/0/DSC_0065%20(rotated).JPG) Dr. Vassilios Krassanakis, 7 March 2016
11
Head stabilization with chin rest
(Pfeiffer, Latoschik, & Wachsmuth, 2008) Dr. Vassilios Krassanakis, 7 March 2016
12
An example of modern eye tracking device
(Source: http://www.tobiipro.com/siteassets/tobii-pro/products/product-commerce-images/ tobiipro_tx300_eye_tracker_front_2_1.jpg) Dr. Vassilios Krassanakis, 7 March 2016
13
Eye tracking glasses
(Source: http://mms.businesswire.com/media/20141219005468/en/446323/5/15405230130_34a80b5bec_o.jpg)
Dr. Vassilios Krassanakis, 7 March 2016
14
Portable & low cost eye trackers
(Source: https://theeyetribe.com/wp-content/uploads/2013/09/blogpost3.jpg)
Dr. Vassilios Krassanakis, 7 March 2016
15
Eye tracking applications psychology landscape perception
graphical user interface evaluation
computer vision
sports vision
medical research
neuroscience
text reading
Eye tracking
web page reading
cartography
advertising
driving behavior art perception
Dr. Vassilios Krassanakis, 7 March 2016
human computer interaction
16
Steps of a typical eye tracking experiment research question experimental design eye movements recording
eye movements analysis results
Dr. Vassilios Krassanakis, 7 March 2016
17
Eye movements recordings ¡ Recordings
of the spatiotemporal coordinates (x,y,t) produced during visual scene* reading process.
¡ Recordings
peripheral).
¡ High
of the central vision (not
frequency recordings (30-2000Hz).
*visual scene: a scene provided on a computer monitor, an image projected to a surface, real world or virtual reality Dr. Vassilios Krassanakis, 7 March 2016
18
Eye tracking data as a (simple) trace Red crosses correspond to records
Dr. Vassilios Krassanakis, 7 March 2016
(Krassanakis, 2009; Krassanakis, Filippakopoulou, & Nakos, 2011a; 20011b)
19
Eye tracking data: A complicated trace 1/3
Observation of 9 fixed points on a computer monitor
(Krassanakis, Filippakopoulou, & Nakos, 2014) Dr. Vassilios Krassanakis, 7 March 2016
(Krassanakis, 2009) 20
Eye tracking data: A complicated trace 2/3
(Krassanakis, Filippakopoulou, & Nakos, 2014) Dr. Vassilios Krassanakis, 7 March 2016
21
Eye tracking data: A complicated trace 3/3
5 targets
(Krassanakis, 2014) Dr. Vassilios Krassanakis, 7 March 2016
22
Eye tracking data as ASCII files: Raw data ¡
Time
¡
Horizontal coordinate
¡
Vertical coordinate
¡
Delta time
¡
Pupil Width
¡
Pupil Aspect
¡
Torsion
¡
…
Dr. Vassilios Krassanakis, 7 March 2016
23
Eye movement analysis Quantitative ¡ Analysis
of eye movements recordings in fundamental and derived metrics.
¡ Fundamental
Saccades
¡ Main
metrics: Fixations &
derived metric: Scanpath
Qualitative ¡ Eye Dr. Vassilios Krassanakis, 7 March 2016
movements visualizations 24
Fixation detection: the critical process of analysis time horizontal coordinate
fixations
vertical coordinate
raw data x1, y1, t1 x2, y2, t2 . . xn, yn, tn
fixations list a 1, b 1, d 1 a 2, b 2, d 2 . . a k, b k, d k
x, y, a, b: coordinates, ti: passing time, di: fixation duration Dr. Vassilios Krassanakis, 7 March 2016
25
Basic eye movement analysis in simple steps
raw data
point cloud clustering
scanpath modeling fixation
saccade (Krassanakis, 2014) Dr. Vassilios Krassanakis, 7 March 2016
26
Events of a typical fixation
(Goldberg & Kotval, 1999)
Dr. Vassilios Krassanakis, 7 March 2016
27
Mean fixation duration and saccade length Task
Mean fixation duration in ms
Mean saccade size in degrees
Silent reading
225
2 (about 8 letters)
Oral reading
275
1.5 (about 6 letters)
Visual search
275
3
Scene perception
330
4
Music reading
375
1
Typing
400
1 (about 4 letters)
(Ryaner, 1998) Dr. Vassilios Krassanakis, 7 March 2016
28
Fixation clustering 1/2 Note: Eyes are relative stationary during a fixation event Cluster definition: an example
(Goldberg & Kotval, 1999) Dr. Vassilios Krassanakis, 7 March 2016
29
Fixation clustering 2/2 Spatial and temporal constraints are required.
(Goldberg & Kotval, 1999)
Dr. Vassilios Krassanakis, 7 March 2016
30
Detecting fixations in eye tracking protocols 1/3
raw data visual trace
Dr. Vassilios Krassanakis, 7 March 2016
31
Detecting fixations in eye tracking protocols 2/3
cluster 1
Dr. Vassilios Krassanakis, 7 March 2016
cluster 2
32
Detecting fixations in eye tracking protocols 3/3
saccade 1 fixation 2
fixation 1
radius represents the duration of the fixation
Dr. Vassilios Krassanakis, 7 March 2016
33
Fixation detection algorithms ¡ Velocity-Based
Algorithms
¡ Dispersion-Based
¡ Area-Based
Algorithms
Algorithms
(Salvucci & Goldberg, 2000)
Dr. Vassilios Krassanakis, 7 March 2016
34
A comparison among fixation identification methods (subcategories are also presented)
(Salvucci & Goldberg, 2000)
Dr. Vassilios Krassanakis, 7 March 2016
35
EyeMMV’s algorithm: an example of fixation detection process ¡ Based
on two spatial parameters (t1,t2) and one temporal parameter that corresponds to the minimum duration.
Example: 5 records distribution
raw data: (x1, y1, pt1), (x2, y2, pt2), (x3, y3, pt3), (x4, y4, pt4), (x5, y5, pt5) x: horizontal coordinate, y: vertical coordinate, pt: passing time Dr. Vassilios Krassanakis, 7 March 2016
36
EyeMMV’s algorithm: spatial parameter t1
d1, d2, d3, d4 < t1 d: Euclidean distances
Ft1 represents the mean point of the cluster Dr. Vassilios Krassanakis, 7 March 2016
37
EyeMMV’s algorithm: spatial parameter t2 check records’ distances from mean point according to t2 parameter
Ft1 represents the mean point of the cluster Dr. Vassilios Krassanakis, 7 March 2016
38
EyeMMV’s algorithm: fixation center after t1& t2
Ft2 represents the mean point of the new cluster which corresponds to fixation’s center
Dr. Vassilios Krassanakis, 7 March 2016
39
EyeMMV’s algorithm: minimum duration parameter Suppose (example) 7 fixation points after the implementation of the spatial parameters. fixation list (after t1,t2) x1, y1, d1 x2, y2, d2 x3, y3, d3 x4, y4, d4 x5, y5, d5 x6, y6, d6 x7, y7, d7
d1, d2, d4, d5, d7 > minimum duration d3, d6 < minimum duration
final list x 1, y1, d 1 x 2, y2, d 2 x 4, y4, d 4 x 5, y5, d 5 x 7, y7, d 7
x: horizontal coordinate, y: vertical coordinate, d: fixation duration Dr. Vassilios Krassanakis, 7 March 2016
40
The importance of fixation analysis ¡ Fixation
analysis affects directly next step analysis (computation of derived metrics, visualizations etc.).
¡ Fixation
parameters are depended on the procedure of the experimentation (e.g. task-specific or free viewing conditions etc.). Adaptations are needed.
¡ The
knowledge of raw data recording quality is also required.
Dr. Vassilios Krassanakis, 7 March 2016
41
Comparing different setups
(Ooms, Dupont, Lapon, & Popelka, 2015) Dr. Vassilios Krassanakis, 7 March 2016
42
Calibration check procedures 1/2
(Krassanakis, 2009) Dr. Vassilios Krassanakis, 7 March 2016
43
Calibration check procedures 2/2 Based on fuzzy c-means clustering
(Krassanakis, 2014)
Dr. Vassilios Krassanakis, 7 March 2016
44
Noise measurement in eye tracking data 1/2
Based on artificial eyes’ measurements.
(Krassanakis, 2014; Krassanakis, Filippakopoulou, & Nakos, 2016)
Dr. Vassilios Krassanakis, 7 March 2016
45
Noise measurement in eye tracking data 2/2
Reporting spatial and temporal noise for each eye (for binocular systems)
(Krassanakis, 2014; Krassanakis, Filippakopoulou, & Nakos, 2016) Dr. Vassilios Krassanakis, 7 March 2016
46
Main and derived metrics ü coordinates ü durations Fixations ü start/end time ü total number ü mean duration ü time to first fixation ü list of repeat fixations ü total duration
Saccades
Scan path
ü total number ü saccade list ü start/end fixation point ü duration ü amplitude ü direction angle
ü length ü duration ü saccades/fixations ratio ü spatial density ü transition matrix ü transition density * The list is not exhaustive. Here only the metrics supported by EyeMMV toolbox (Krassanakis et al., 2014) are presented. 47 Dr. Vassilios Krassanakis, 7 March 2016
Metrics example: scanpath duration & length
(Goldberg & Kotval, 1999) Dr. Vassilios Krassanakis, 7 March 2016
48
Metrics example: scanpath spatial density
(Goldberg & Kotval, 1999) Dr. Vassilios Krassanakis, 7 March 2016
49
Metrics example: scanpath transition density
(Goldberg & Kotval, 1999) Dr. Vassilios Krassanakis, 7 March 2016
50
Visualizations of eye tracking data Raw data: ¡ Raw
data distribution: (x,y)
¡ Horizontal
and vertical coordinates along time: (x,t) & (y,t)
¡ Space-time-cube
visualization: (x,y,t)
Analyzed data: ¡ Scanpath
visualization
¡ Heatmap
visualization
¡ … Dr. Vassilios Krassanakis, 7 March 2016
51
Raw data distribution
(Larsson, 2010) Dr. Vassilios Krassanakis, 7 March 2016
52
Scanpath example
Dr. Vassilios Krassanakis, 7 March 2016
(Source: http://www.usability.de/assets/img/content/m-et-gazeplot.jpg)
53
Multiple scanpaths
Dr. Vassilios Krassanakis, 7 March 2016
(Eraslan, Yesilada, & Harper, 2016)
54
Heatmaps
(Source: http://www.neurotechnology.com/res/sentigaze-web-heatmap.jpg) Dr. Vassilios Krassanakis, 7 March 2016
55
Heatmap construction
(Krassanakis, 2014) Dr. Vassilios Krassanakis, 7 March 2016
56
Multiple scanpaths vs Heatmap
(Source: http://www.profitippliga.de/toreyetracking.jpg)
Dr. Vassilios Krassanakis, 7 March 2016
57
Space time cube for video eye tracking data
(Blascheck, Kurzhals, Raschke, Burch, Weiskopf, & Ertl, 2014)
Dr. Vassilios Krassanakis, 7 March 2016
58
Eye tracking analysis software ¡ Main
function: fixations/saccades identification (identification algorithms implementation)
¡ Analysis
metrics
¡ Eye
of eye tracking data in derived
tracking visualization techniques
¡ Regions
of Interest (ROIs) analysis
¡ …
Dr. Vassilios Krassanakis, 7 March 2016
59
Freely available eye tracking tools ¡ ILAB ¡ Eyelink
¡ ITU
Toolbox
Gaze Tracker
¡ GazeAlyze
¡ iComp
¡ EHCA
Toolbox
¡ openEyes
¡ GazeParser
¡ eyePatterns
¡ EyeMMV
¡ iComponent
¡ ETRAN-R
¡ OGAMA
¡ …
toolbox
(modified after Krassanakis, Filippakopoulou, & Nakos, 2014) Dr. Vassilios Krassanakis, 7 March 2016
60
Example 1: OGAMA ¡ Open
Gaze And Mouse Analyzer (Voßkühler, Nordmeier, Kuchinke, & Jacobs, 2008)
¡ Freeware,
C# .NET
¡ Graphical
User Interface (GUI)
¡ Several
modules (metrics, statistics, visualizations etc.)
¡ Fully
documented
¡ Supports
a lot of commercial and open source eye trackers.
¡ Supports
saliency calculations (based on Itti & Koch, 2001).
Dr. Vassilios Krassanakis, 7 March 2016
61
OGAMA: Stimulus design module
(Source: http://www.ogama.net) Dr. Vassilios Krassanakis, 7 March 2016
62
OGAMA: Recording module
(Source: http://www.ogama.net) Dr. Vassilios Krassanakis, 7 March 2016
63
OGAMA: Fixations module
(Source: http://www.ogama.net) Dr. Vassilios Krassanakis, 7 March 2016
64
OGAMA: Statistics module
(Source: http://www.ogama.net) Dr. Vassilios Krassanakis, 7 March 2016
65
OGAMA: Saliency module
(Source: http://www.ogama.net) Dr. Vassilios Krassanakis, 7 March 2016
66
Example 2: EyeMMV toolbox ¡ Eye
Movements Metrics & Visualizations (Krassanakis, Filippakopoulou, & Nakos 2014)
¡ Open
source MATLAB code (under GPLv3)
¡ Complete
toolbox
post-experimental analysis
¡ Command
line toolbox (list of functions)
¡ Cross-platform
installed)
Dr. Vassilios Krassanakis, 7 March 2016
(where MATLAB is pre67
EyeMMV’s fixation identification algorithm: flowchart
Dr. Vassilios Krassanakis, 7 March 2016
(Krassanakis, 2014)
68
EyeMMV toolbox: fixation detection report
(Krassanakis, 2013a) Dr. Vassilios Krassanakis, 7 March 2016
69
EyeMMV toolbox: fixation/saccade metrics
(Krassanakis, 2013a) Dr. Vassilios Krassanakis, 7 March 2016
70
EyeMMV toolbox: scanpath analysis
(Krassanakis, 2013a) Dr. Vassilios Krassanakis, 7 March 2016
71
EyeMMV toolbox: visualizations
(Krassanakis, Filippakopoulou, & Nakos, 2014)
Dr. Vassilios Krassanakis, 7 March 2016
72
EyeMMV toolbox: heatmap visualization and ROIs analysis
(Krassanakis, Filippakopoulou, & Nakos, 2014)
Dr. Vassilios Krassanakis, 7 March 2016
73
Research studies: examples Experimentation in the field of map perception: ¡ Design
(visual and dynamic) variables
¡ Change
detection
¡ Post-detection ¡ Cartographic ¡ Modeling
Dr. Vassilios Krassanakis, 7 March 2016
reaction
generalization
map users gaze behavior 74
Research study 1 ¡ Examination
of the topological feature of hole as preattentive feature.
¡ Quantitative
tracking.
analysis based on eye
¡ Repeat
experimentation made by capturing Reaction Time (RT). targets
Dr. Vassilios Krassanakis, 7 March 2016
distractors
75
Research study 1: Stimuli ¡ Cartographic
backgrounds
(Krassanakis, 2009; 2013b; Krassanakis, Filippakopoulou, & Nakos, 2011a; 2011b)
Dr. Vassilios Krassanakis, 7 March 2016
76
Research study 1: Results 1/2 ¡ Based
on scanpaths examination
(Krassanakis, 2009, 2013b; Krassanakis, Filippakopoulou, & Nakos, 2011a, 2011b) Dr. Vassilios Krassanakis, 7 March 2016
77
Research study 1: Results 2/2 ¡ Visual
search starts from previous location.
¡ More
complicated scanpath generated on map periphery.
¡ Fixations
correspond to point symbols locations.
¡ Saccades
correspond to the transitions through targets and distractors.
¡ In
case of target-absent trials, more complicated scanpath are observer (no special pattern).
Dr. Vassilios Krassanakis, 7 March 2016
78
Research study 2 ¡ Preliminary
study
¡ Examination
of map readers reaction to the variables of duration and rate of change.
(Krassanakis, Lelli, Lokka, Filippakopoulou, & Nakos 2013a) Dr. Vassilios Krassanakis, 7 March 2016
79
Research study 2: symbol detection
(Krassanakis, Lelli, Lokka, Filippakopoulou, & Nakos, 2013a) Dr. Vassilios Krassanakis, 7 March 2016
80
Research study 2: Preliminary results
(Krassanakis, Lelli, Lokka, Filippakopoulou, & Nakos, 2013a; Krassanakis, 2013) Dr. Vassilios Krassanakis, 7 March 2016
81
Research study 3 ¡ Examination
of the minimum duration threshold required for the detection by the central vision of a moving point symbol on cartographic backgrounds
(Krassanakis, 2014; Krassanakis, Filippakopoulou, & Nakos, 2016) Dr. Vassilios Krassanakis, 7 March 2016
82
Research study 3: Analysis approach Definition of statistical metrics based on fixations: ¡ Success
metric
¡ Duration
metric
¡ Duration
percentage metric
¡ Number
metric
¡ Number
percentage metric
¡ Time Dr. Vassilios Krassanakis, 7 March 2016
to first fixation metric 83
Research study 3: Results 1/2 Explanation of “overall visual reaction” through regression analysis
(Krassanakis, 2014; Krassanakis, Filippakopoulou, & Nakos, 2016) Dr. Vassilios Krassanakis, 7 March 2016
84
Research study 3: Results 2/2 Duration threshold computation
(Krassanakis, 2014; Krassanakis, Filippakopoulou, & Nakos, 2016)
Dr. Vassilios Krassanakis, 7 March 2016
85
Research study 4 ¡ Post-detection
examination
¡ Stimuli:
blank background and cartographic background
¡ Results
are compared (quantitatively) with the corresponded outcomes of saliency models implementation.
¡ Saliency
models: developed to predict human vision behavior and based on natural scenes’ tests.
Dr. Vassilios Krassanakis, 7 March 2016
86
Research study 4: Results (Itti, Koch, & Niebur, 1998)
(Harel, Koch, & Perona, 2006)
(Hou, Harel, & Koch, 2012]
(Krassanakis, Lelli, Lokka, Filippakopoulou & Nakos 2013b) Dr. Vassilios Krassanakis, 7 March 2016
87
Research study 5 ¡ Comparison
of critical points’ locations with fixation points.
Cartographic generalization: 1:k initial line
generalized line Dr. Vassilios Krassanakis, 7 March 2016
1:10k 88
Research study 5: stimuli
(Bargiota, 2013) Dr. Vassilios Krassanakis, 7 March 2016
89
Research study 5: Results 1/2
(Bargiota, Mitropoulos, Krassanakis, & Nakos, 2013) Dr. Vassilios Krassanakis, 7 March 2016
90
Research study 5: Results 2/2
Detection of critical points based on “LR method”(ALR Index) (Nakos & Mitropoulos, 2005)
(Bargiota, Mitropoulos, Krassanakis, & Nakos, 2013)
(Bargiota, 2013) Dr. Vassilios Krassanakis, 7 March 2016
91
Research study 6 ¡ Introduction
of a new visualization method of eye tracking data based on polylines inferred from samples’ analysis.
Dr. Vassilios Krassanakis, 7 March 2016
(Karagiorgou, Krassanakis, Vescoukis, & Nakos, 2014)
92
Research study 6: Results
(Karagiorgou, Krassanakis, Vescoukis, & Nakos, 2014) Dr. Vassilios Krassanakis, 7 March 2016
93
Thank you
Dr. Vassilios Krassanakis, 7 March 2016
Dr. Vassilios Krassanakis
[email protected] https://sites.google.com/site/vassilioskrassanakis/
94
References 1/3 Bargiota T. (2013). Measuring critical points along cartographic lines using eye movements (In Greek). Master Thesis, Master of Science Program in Geoinformatics, National Technical University of Athens. Bargiota T., Mitropoulos V., Krassanakis V., & Nakos B. (2013). Measuring locations of critical points along cartographic lines with eye movement analysis. Proceeding of the 26th International Cartographic Conference. Dresden, Germany. Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., & Ertl, T. (2014). State-of-the-art of visualization for eye tracking data. In: Proceedings of EuroVis (Vol. 2014). Duchowski, A. (2007). Eye tracking methodology: Theory and practice. London: Springer. Eraslan, S., Yesilada, Y., & Harper, S. (2016). Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison. Journal of Eye Movement Research 9(1):2, 1-19. Goldberg, J.H., & Kotval, X.P. (1999). Computer interface evaluation using eye movements: methods and constructs. International Journal of Industrial Ergonomics, 24(6), 631-645. Harel J., Koch C., & Perona P. (2006). Graph-Based Visual Saliency. In: Advances in neural in- formation processing systems, 545-552. Hou X., Harel J., & Koch C. (2012), Image Signature: Highlighting Sparse Salient Regions, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(1): 194-201. Itti L. Koch C., & Niebur E. (1998). A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11): 1254- 1259. Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature reviews neuroscience, 2(3), 194-203. Karagiorgou S., Krassanakis V., Vescoukis V., & Nakos B. (2014), Experimenting with polylines on the visualization of eye tracking data from observations of cartographic lines, In: P. Kiefer, I. Giannopoulos, M. Raubal, A. Krüger (Eds.), Proceedings of the 2nd International Workshop on Eye Tracking for Spatial Research (co-located with the 8th International Conference on Geographic Information Science (GIScience 2014)), Vienna, Austria, 22-26, pp. 22-26. Dr. Vassilios Krassanakis, 7 March 2016
95
References 2/3 Krassanakis V. (2014). Development of a methodology of eye movement analysis for the study of visual perception in animated maps (In Greek). Doctoral Dissertation, School of Rural and Surveying Engineering, National Technical University of Athens. Krassanakis V., (2013a). Using EyeMMV Toolbox for eye movement analysis in cartographic experiments, Workshop of International Cartographic Association, Eye Tracking: Why, When, and How?, Dresden, Germany. Krassanakis V., (2013b). Exploring the map reading process with eye movement analysis. In P. Kiefer, I. Giannopoulos, M. Raubal, & M. Hegarty, eds. Eye Tracking for Spatial Research, Proceedings of the 1st International Workshop (in conjunction with COSIT 2013). Scarborough, United Kingdom, pp. 2-7. Krassanakis V., Lelli A., Lokka I. E., Filippakopoulou V., & Nakos B. (2013b). Searching for salient locations in topographic maps. In T. Pfeiffer & K. Essig, eds. Proceedings of the First International Workshop on Solutions for Automatic Gaze Data Analysis 2013 (SAGA 2013). Bielefeld, Germany: Center of Excellence Cognitive Interaction Technology, pp. 41-44. Krassanakis V., Lelli A., Lokka I. E., Filippakopoulou V., & Nakos B., (2013a). Investigating dynamic variables with eye movement analysis. Proceeding of the 26th International Cartographic Conference. Dresden, Germany. Krassanakis, V. (2009). Recording the trace of visual search: a research method of the selectivity of hole as a basic shape characteristic (In Greek). Diploma Thesis, School of Rural and Surveying Engineering, National Technical University of Athens. Krassanakis, V., Filippakopoulou, V., & Nakos, B. (2011a). The influence of attributes of shape in map reading process. Proceedings of the 25th International Cartographic Conference. Paris, France. Krassanakis, V., Filippakopoulou, V., & Nakos, B. (2011b). An Application of Eye Tracking Methodology in Cartographic Research. Proceedings of the EyeTrackBehavior 2011 (Tobii). Frankfurt, Germany. Krassanakis, V., Filippakopoulou, V., & Nakos, B. (2014). EyeMMV toolbox: An eye movement post-analysis tool based on a two-step spatial dispersion threshold for fixation identification. Journal of Eye Movement Research, 7(1): 1, 1-10. Dr. Vassilios Krassanakis, 7 March 2016
96
References 3/3 Krassanakis, V., Filippakopoulou, V., & Nakos, B. (2016). Detection of moving point symbols on cartographic backgrounds. Journal of Eye Movement Research, 9(2):2, 1-16. Larsson, L. (2010). Event detection in eye-tracking data. Master’s thesis, Lund University, Lund, Sweden. Ooms, K., Dupont, L., Lapon, L., & Popelka, S. (2015). Accuracy and precision of fixation locations recorded with the low-cost Eye Tribe tracker in different experimental set- ups. Journal of Eye Movement Research 8(1):5, 1-24. Pfeiffer, T., Latoschik, M. E., & Wachsmuth, I. (2008). Evaluation of binocular eye trackers and algorithms for 3D gaze interaction in virtual reality environments. JVRB-Journal of Virtual Reality and Broadcasting, 5(16). Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological bulletin, 124(3), 372-422. Salvucci, D.D., & Goldberg, J.H. (2000) November. Identifying fixations and saccades in eye-tracking protocols. In: Proceedings of the 2000 symposium on Eye tracking research & applications (pp. 71-78). ACM. Strupp, M., Kremmyda, O., Adamczyk, C., Böttcher, N., Muth, C., Yip, C. W., & Bremova, T. (2014). Central ocular motor disorders, including gaze palsy and nystagmus. Journal of neurology, 261(2), 542-558. Voßkühler, A., Nordmeier, V., Kuchinke, L. and Jacobs, A.M., 2008. OGAMA (Open Gaze and Mouse Analyzer): open-source software designed to analyze eye and mouse movements in slideshow study designs. Behavior research methods, 40(4), 1150-1162. Young, L. R., & Sheena, D. (1975). Survey of eye movement recording methods. Behavior research methods & instrumentation, 7(5), 397-429.
Dr. Vassilios Krassanakis, 7 March 2016
97