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136 results for “eye tracking”
QAVA-DPC: Eye-Tracking Based Quality Assessment and Visual Attention Dataset for Dynamic Point Cloud in 6 DoF
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Assessing the impact of central and peripheral obstructions on visual behavior: insights from gaze-contingent eye-tracking studies
<p>This dataset is a collection of image stimuli, as well as gaze tracks collected using visual field masks, such as central and peripheral scotoma, in order to assess the way people with visual field loss process visual stimuli in digital environments.</p> <h3><strong>This dataset is structured as follows :</strong></h3> <ul> <li>Stimuli: 125 fullHD images (1920x1080) used during the experiment trials; 4 fullHD images used during the training phase.</li> <li>Trials_metadata: <ul> <li>Psychopy_outputs: various callbacks and logs files from the Psychopy experiment</li> <li>trials_metadata: .csv files containing metadata such as stimuli order and timestamps.</li> </ul> </li> <li>Raw_eyetracking_outputs: subfolders containing the raw .edf files output by the Eyelink 1000+ eye-tracker. .edf files are divided into groups of 25 successive stimuli displayed.</li> <li>Raw_gaze_points: .csv files, containing the raw gaze points locations for each subject, stimulus and mask. Blinks, invalid coordinates and out-of-bounds gaze points are already removed from these files.</li> <li>Fixations: .csv files (one per image per participant) containing eye fixations locations extracted from the raw gaze points using a I-VT algorithm with a saccade velocity threshold of 45 deg/s.</li> </ul> <h2><br><strong>Experiment</strong></h2> <h3>Images</h3> <p>This database consists of 100 images collected from personal collections and various public image datasets, such as the CityScapes and the KITTI-360 datasets, covering a variety of themes, including landscapes, people, actions, and nature.<br>Images had a 1920 x 1080 pixel resolution (FullHD) and were shown on a screen with the same resolution. </p> <h3>Eye-tracking data collection</h3> <p>The stimuli were presented to a group of 37 observers, with normal or corrected-to-normal vision. We used a table-mounted EyeLink 1000 Plus eye-tracker, working at a fixed rate of 1000Hz, with a chin rest to ensure data accuracy. Participants were informed that they would observe images both with and without simulated visual field impairments, and the two types of masks used were described. They were also told that the only task is to view the images freely. </p> <p>To familiarize participants with the different types of masks, four training images (distinct from the trial dataset) were provided in the training phase, with each image presented in the three conditions -- peripheral mask, foveal mask, and control (no mask) -- with varying mask sizes. <br>In the trial phase, stimuli were displayed in a random order, with a randomly generated playlist ensuring no three consecutive stimuli were of the same image. A uniform gray screen was displayed between each stimulus. </p> <p>Viewing distance was set to be 90cm.<br>Eye-tracker calibration was carried out using a 9-point calibration protocol, i.e., 9 points were sequentially and randomly shown on the screen, where the observer should fixate their gaze. <br>Additionally, calibration was performed after every 25 displayed images.</p> <p>Each image was presented to the observers for a 5-second period.</p> <p>Each image in the database was presented to the observers under different conditions :<br> - The image without any obstruction, referred to as the control condition (C). <br> - Peripheral mask (P) simulating a tunnel vision, with two circular mask size variations (one individual mask for each eye): 1.5° and 4.5° radius of field of view, referred to as P1 and P2, respectively. <br> - Foveal mask (F) simulating a central scotoma, also with two size variations: 1.5° and 4.5° radius of obstruction, referred to as F1 and F2, in that order.</p> <p> </p>
Las técnicas de eye-tracking aplicadas al estudio experimental de la eficacia publicitaria de campañas de PRL en la Juventud
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
Experimental meterials for "Identifying Lines and Interpreting Vertical Jumps in Eye Tracking Studies of Reading Text and Code"
<p>Experimental materials for the paper "Identifying Lines and Interpreting Vertical Jumps in Eye Tracking Studies of Reading Text and Code" published in the ACM Transactions on Applied Perception.</p> <p>Includes the texts used in the eye tracking experiments, the data collected by the eye tracker for all the participants, the Python script used to analyze the data, and the graphs produced by this script.</p>
eye-tracking data from a survey on zooming in a pan-scalar map
<p><strong>Recording and processing a survey using an eye tracker </strong></p> <p>The eye-tracker used is a Pupil Core from Pupil Labs. The basic eye tracker configuration, i.e. a fixation time of 80 ms to 200 ms, is kept for this experiment.</p> <p> </p> <p>The aim of the experiment is to understand what a person looks at to find their way around a multi-scale map and to understand the different strategies used. To do this, the user will be free to use the map as he wishes, i.e. he can use pan and zoom at will. Four types of tasks will be asked in order to have a maximum of types of use of multi-scale map.The first task is to simulate that a user is using an application like map or Google map and is looking for a specific address. The map application will then zoom in very strongly on the address. The user has little spatial context and it often takes some time to find his way around. To simulate the application, a point is placed on Paris or its surroundings and the display is very zoomed (Paris was chosen because most people have a more or less detailed mental map of Paris). The user is then asked to interact with the map (zooming and panning) until he feels he is sufficiently located, as he would if he had to search for a place on his mobile phone. When he is located, he just needs to move on to the next stage without asking for validation. This stage is carried out in four locations. The four points are located near Montmartre, at the entrance to the catacombs of Paris, in Vincennes and finally at Porte d'Asnières</p> <p><br> The second task is to find a place from an aerial image. The aerial image of a specific area is displayed and the map is zoomed out to the city where the location is located. The user must then try to find the location in the image. Unlike the first task, the user must request validation before proceeding to the next stage.<br> This task is repeated in two different cities. The two images are the tête d'or park in Lyon and a building block next to a railway in Dijon.</p> <p><br> The third task also consists of finding a precise location using textual indications. The user still has to ask for validation to go to the next stage .</p> <p>This task is repeated in two different cities.The first was "to find the town hall which is just south of the town centre and next to the library" and the second was "to find the stadium east of the town centre and north of the river Vilaine with a north/south orientation.</p> <p><br> The last task builds on tasks 2 and 3. The map is again zoomed out, an aerial image appears and textual indications are given. This task is repeated on two different cities.</p> <p>The first image is of a building in beauvais with the indication: "the building is in the north west of sqare next to the SNCF station". The second one is a picture of a stadium in lyon with the indication: "the stadium is west of the confluence of lyon".</p> <p><strong>data format :</strong><br> <strong>Coord_fixation_on_map_x_y</strong>: geolocated fixation point with x the survey type 1 or 2 and y the candidate number (id_fixation,x,y,zoom,etape)</p> <p><strong>Pan</strong>: pan on the map during the survey</p> <p><strong>Pan_fixation_on_map</strong> : fixation during a pan</p> <p><strong>zoom</strong>: zoom on the map during the survey</p> <p><strong>zoom_fixation_on_map</strong>: fixation during a zoom</p> <p><strong>stat</strong>: number of zoom, pan and fixation per step</p> <p><strong>result_map_x </strong>= map status every 100 ms during the survey x</p> <p><strong>00x </strong>: export file of the eye-tracker pupil Lab</p> <p> </p>
Investigating Visual Perception Impairments through Serious Games and Eye Tracking to Anticipate Handwriting Difficulties - DATASET
<p>In the present dataset, each row represents a subject. For each subject, there are</p> <ul> <li>the ID</li> <li>the gender</li> <li>the class</li> <li>the results in the BVSCO-2 test</li> <li>their position over or under the BVSCO-2 threshold ("prove sopra soglia" represents the number of exercises in which the subject was over the thresold, and "sopra soglia generale" is 1 when a subject is over the threshold in all of the exercises, and 0 otherwise)</li> <li>the features extracted from the game described in the article (times and errors)</li> <li>the features extracted from the data produced by drawing with the Apple Pencil</li> <li>the features extracted from the eye tracker. </li> </ul>
Assessing Code Readability in Python Programming Courses Using Eye-Tracking - Python Code Snippets
<p>Python code snippets for assessing code readability in Python programming courses using eye-tracking.</p>
Supplementary Material - All Eyes on Traceability: An Interview Study on Industry Practices and Eye Tracking Potential
<p>This dataset is the supplementary material for the paper "All Eyes on Traceability: An Interview Study on Industry Practices and Eye Tracking Potential" accepted at RE '23.</p> <p>The PDF titled "Interview-Questions" presents the interview questions used for the semi-structured interview of the paper.<br> The PDF titled "Interview-Questions_ReplicatorVersion" presents the interview questions enriched with comments on the qualitative and quantitative extent of the expected interviewee's responses and the category codes assigned to them during the analysis process.<br> The PDF "Eye-Tracking-Traceability-Explanatory-Slide" contains the Slide used in part 4 of the interview.<br> The spreadsheet "ArtifactsLinked.xlsx" was used to determine the number of interviewees who mentioned particular pairings of artifact types as currently being linked or ideally linked. This is the raw data for Fig. 5 in the paper.</p>
Replication package for "An Exploratory Eye Tracking Study on How Developers Classify and Debug Python Code in Different Paradigms"
<p>See the README.md file for more details.</p>
Improving Performance of Combat Soldiers by Utilizing Attentional Training Based on Eye Tracking
ClinicalTrials.gov study NCT05306197. IPD Sharing: NO. Countries: 1. Publications: 1.
Eye Tracking and Simulated Postpartum Hemorrhage
ClinicalTrials.gov study NCT04395963. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Shift Hours' Impact on Fatigue and Tracking of Eye Dynamics
ClinicalTrials.gov study NCT07192380. IPD Sharing: YES. Countries: 1. Publications: 13.
Immersive Virtual REality for Treatment of Unilateral Spatial NEglect Via Eye-tracking Biofeedback
ClinicalTrials.gov study NCT06264713. IPD Sharing: NO. Countries: 1. Publications: 10.
Eye-tracking Technology for Severe Communication Disability
ClinicalTrials.gov study NCT05808478. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Eye Tracking Technology in the Diagnosis of Neurological Patients
ClinicalTrials.gov study NCT05536921. IPD Sharing: NO. Countries: 1. Publications: 1.
Eye-tracking-based Artificial Intelligence Detects Abnormalities of the Oculomotor System in Type 1 Diabetes
ClinicalTrials.gov study NCT04608890. IPD Sharing: NO. Countries: 1. Publications: 1.
Increasing Psychological Resilience in Combat Soldiers Applying Advanced Eye-Tracking-Based Attention Bias Modification
ClinicalTrials.gov study NCT05294848. IPD Sharing: NO. Countries: 1. Publications: 1.
Visual Attention to Text and Pictorial Food Labels: An Eye Tracking Experiment
ClinicalTrials.gov study NCT05958888. IPD Sharing: YES. Countries: 1. Publications: 0.
The Effects of Losartan on Attention Control: An Eye-tracking Study
ClinicalTrials.gov study NCT06329050. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Contribution of Virtual Reality Eye Tracking in the Identification of Schizophrenia, Bipolar and Depression
ClinicalTrials.gov study NCT07057024. IPD Sharing: Not stated. Countries: 1. Publications: 9.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.