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136 results for “eye tracking”
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
THÖR - eye-tracking
<p><strong>THÖR</strong> is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.</p> <p><strong>THÖR eye-tracking -</strong> data of the participant (Helmet number 9) from the Tobii Glasses included in this dataset. The entire data from the experiment was recorded into two recordings - "Recording011" and "Recording012".</p> <p>The folder "RawData" consists of exported data from Tobii Pro Lab software using a filter called "Tobii-IVT Attention filter" (velocity threshold parameter set to 100 degrees/second), which is a recommended method for dynamic situations. The recommendation was given by the equipment manufactures Tobii Pro and from other researchers. For further information, please refer to https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/Tobii-Pro-Lab-User-Manual/?v=1.86 (Appendix-B, page 85).</p> <p>The recording start times are as follows:<br> Tobii recording011 starttime: 13:34:37.267<br> Tobii recording012 starttime: 14:36:17.730</p> <p>1. "Synchronized_Qualisys_Tobii.mat" file, which consists of all the synchronised data between Qualisys data and Tobii eye-tracker data using timestamps matching. The columns (headers) in this mat file respectively represent - 'timestamp', 'Pos_X', 'Pos_Y', 'Pos_Z', 'Head_R', 'Head_P', 'Head_Y', 'GazepointX', 'GazepointY', 'Gazepoint3D_X', 'Gazepoint3D_Y', 'Gazepoint3D_Z', 'Gazedirectionleft_X', 'Gazedirectionleft_Y', 'Gazedirectionleft_Z', 'Gazedirectionright_X', 'Gazedirectionright_Y', 'Gazedirectionright_Z', 'Pupilpositionleft_X', 'Pupilpositionleft_Y', 'Pupilpositionleft_Z', 'Pupilpositionright_X', 'Pupilpositionright_Y', 'Pupilpositionright_Z', 'Pupildiameterleft', 'Pupildiameterright', 'Gazeeventduration', 'Eyemovementtype_index', 'Fixationpoint_X', 'Fixationpoint_Y', 'Gyro_X', 'Gyro_Y', 'Gyro_Z', 'Accelerometer_X', 'Accelerometer_Y', 'Accelerometer_Z'.</p> <p>2. A matlab script "Synchronizing_Qualisys_Tobii.m" used for matching the timestamps of Qualisys and Tobii and generate a data file "Synchronized_Qualisys_Tobii.mat" that can be found it git repository.</p> <p>3. All the associated data required for running the matlab script.</p> <p>Please note that 'Timestamp', 'Pos_X', 'Pos_Y', 'Pos_Z', 'Head_R', 'Head_P', 'Head_Y','GazepointX', 'GazepointY' represent the timestamps (matched to Tobii timestamps using nearest neighbor search), position and head orientation from Qualisys data and rest of the data is from Tobii eye-tracker. For more information regarding the eye-tracker data, please refer to https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/Tobii-Pro-Lab-User-Manual/?v=1.86 (Section 8.7.2.1, page 68).</p> <p> </p>
More precise tracking of horizontal than vertical target motion with both the eyes and hand
<p>Those files contain individual data from a large cohort of participant (N=62). </p> <p>In the excel file (DATAmain), each sheet presents one set of variables (with individual value for each trial).</p> <p>This file contains information regarding eye and hand tracking performance (distance+lags), as well as smooth pursuit gains. </p> <p>The other files contain data that we used for the detailed analysis of saccades and lags, as well as the scripts that can be run with Perl. One script is for analysing the lag (Danion.pl) and the other one for analysing the saccades (saccades.pl). The other files (.txt and .dat) that were used for these analyses. Note that some library is needed (common_subroutines, draw_figure, and for the anova’s routines_that_use_R), meaning that you need to have R installed. </p> <pre>Regarding data acquisition we employed a program called Docometre that can be uploaded at the following address: http://139.124.68.1/buloup/index.php?selectedMenu=DOCoMETRe&lang=_fr When this program is installed, it needs to be run with BaselineTracking.dcm We also provide .BAS and .T91 files that correspond to the compiled version of each pattern Regarding visual stimuli, another program called ICE needs to be installed on a separate computer that receives information (target+cursor) from docometer, it can be uploaded at : https://trello.com/b/EtNCNrZH/icehttps://trello.com/b/EtNCNrZH/ice ICE needs to be run with Visuomotor.ice Visuomotor.icepro Visuomotor.txt and Visuomotor.icemat in the respective folder (icepro in Protocol folder, icemat and ice in Scenario Folder, and txt in Serie folder) Note that both Docometre and ICE need to be run with similar equipement as our (including Adwin Gold systems, Megatron joystick, video screen, graphic cards, and desktop eyelink providing analog signals to docometre). Adequate numbering of analogic channels needs also to be ensured. </pre>
Eye tracking videos and raw data of breathing recognition attempts in simulated out-of-hospital cardiac arrest
<div> <div> <div> <p>This dataset comprises eye tracking videos and raw data documenting attempts to recognize breathing in simulated out-of-hospital cardiac arrest scenarios.</p> <p>The data were recorded using an Ergoneers Dikablis head-mounted eye tracker.</p> <p>Our analysis of this data resulted in the publication of two studies: Study 1, available at <a href="https://doi.org/10.1097/SIH.0000000000000617" target="_blank" rel="noopener">https://doi.org/10.1097/SIH.0000000000000617</a>, and Study 2, accessible at <a href="https://doi.org/10.25894/ijfae.2307" target="_blank" rel="noopener">https://doi.org/10.25894/ijfae.2307</a></p> <p> </p> <p>Version 2 is up-to-date.</p> <p>In Version 1:</p> <ul> <li>the doi for Study 2 was incorrect</li> <li>data for participant #51 of Study 1 were missing</li> </ul> </div> </div> </div>
A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning
<p>Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high.</p> <p>Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis.</p> <p><span>Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism</span><span>. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.</span></p>
The INI-30 Dataset : Event Camera for Eye Tracking
<p>The Ini-30 dataset is collected with two event cameras mounted on a glass frame. Each DVXplorer sensor (640 × 480 pixels) is attached on the side of the frame. The power supply was provided via a 2 meter cable connected from the cameras to a computer, which provided enough freedom of movement. Differently from [2, 24], the participants were not instructed to follow a dot on a screen, but rather encouraged to look around to collect natural eye movements. As shown in Fig. 1, the event cameras were securely screwed on a 3D-printed case attached to the side of the glass frame. The data was annotated based on accumulated linearly decayed events by defining the pixel intensity as function of the linear accumulation of previous pixel intensity. Next we labeled the position of the pupil in the DVS’s array manually, using an assistive labeling tool. We discarded the first 20ms of events to ensure the eye was visible and annotations met the level of image-based annotators. The number of labels per recording was intentionally variable, spanning from 475 to 1’848 with a time per label ranging from 20.0 to 235.77 milliseconds depending on the overall duration of the sample. This setup allows for unconstrained head movements, enables to capture event data from eye movement in a ”in-the-wild” setting and allows the generation of a representative, unique, diverse and challenging dataset.<br><br>NOTE : the annotations relates to the ellipse of the pupil on the image</p>
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
<p>Version 4 of the dataset is available (Sep 19 2019)!</p> <p>Note this version has significantly more data than Version 2. </p> <p>Dataset description paper (full version) is available!</p> <p>https://arxiv.org/pdf/1903.06754.pdf (updated Sep 7 2019)</p> <p>Tools for visualizing the data is available!</p> <p>https://github.com/corgiTrax/Gaze-Data-Processor</p> <p> </p> <p><strong>=========================== Dataset Description ===========================</strong></p> <p>We provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded.</p> <p> </p> <p>Q & A: Why frame-by-frame game mode?</p> <p><strong>Resolving state-action mismatch</strong>: Closed-loop human visuomotor reaction time is around 250-300 milliseconds. Therefore, during gameplay, state (image) and action that are simultaneously recorded at time step t could be mismatched. Action at time t could be intended for a state 250-300ms ago. This effect causes a serious issue for supervised learning algorithms, since label at and input st are no longer matched. Frame-by-frame game play ensures states and actions are matched at every timestep.</p> <p><strong>Maximizing human performance</strong>: Frame-by-frame mode makes gameplay more relaxing and reduces fatigue, which could normally result in blinking and would corrupt eye-tracking data. More importantly, this design reduces sub-optimal decisions caused by inattentive blindness.</p> <p><strong>Highlighting critical states that require multiple eye movements</strong>: Human decision time and all eye movements were recorded at every frame. The states that could lead to a large reward or penalty, or the ones that require sophisticated planning, will take longer and require multiple eye movements for the player to make a decision. Stopping gameplay means that the observer can use eye-movements to resolve complex situations. This is important because if the algorithm is going to learn from eye-movements it must contain all “relevant” eye-movements.</p> <p> </p> <p><strong>============================ Readme ============================</strong></p> <p>1. meta_data.csv: meta data for the dataset., including:</p> <ul> <li> <p>GameName: String. Game name. e.g., “alien” indicates the trial is collected for game Alien (15 min time limit). “alien_highscore” is the trajectory collected from the best player’s highest score (2 hour limit). See dataset description paper for details.</p> </li> </ul> <ul> <li> <p>trial_id: Integer. One can use this number to locate the associated .tar.bz2 file and label file.</p> </li> <li> <p>subject_id: Char. Human subject identifiers.</p> </li> <li> <p>load_trial: Integer. 0 indicates that the game starts from scratch. If this field is non-zero, it means that the current trial continues from a saved trial. The number indicates the trial number to look for.</p> </li> <li> <p>highest_score: Integer. The highest game score obtained from this trial.</p> </li> <li> <p>total_frame: Number of image frames in the .tar.bz2 repository.</p> </li> <li> <p>total_game_play_time: Integer. game time in ms. </p> </li> <li> <p>total_episode: Integer. number of episodes in the current trial. An episode terminates when all lives are consumed.</p> </li> <li> <p>avg_error: Float. Average eye-tracking validation error at the end of each trial in visual degree (1 visual degree = 1.44 cm in our experiment). See our paper for the calibration/validation process.</p> </li> <li> <p>max_error: Float. Max eye-tracking validation error. </p> </li> <li> <p>low_sample_rate: Percentage. Percentage of frames with less than 10 gaze samples. The most common reason for this is blinking.</p> </li> <li> <p>frame_averaging: Boolean. The game engine allows one to turn this on or off. When turning on (TRUE), two consecutive frames are averaged, this alleviates screen flickering in some games.</p> </li> <li> <p>fps: Integer. Frame per second when an action key is held down.</p> </li> </ul> <p> </p> <p>2. [game_name].zip files: these include data for each game, including:</p> <p>*.tar.bz2 files: contains game image frames. The filename indicates its trial number.</p> <p>*.txt files: label file for each trial, including:</p> <ul> <li> <p>frame_id: String. The ID of a frame, can be used to locate the corresponding image frame in .tar.bz2 file.</p> </li> <li> <p>episode_id: Integer (not available for some trials). Episode number, starting from 0 for each trial. A trial could contain a single trial or multiple trials.</p> </li> <li> <p>score: Integer (not available for some trials). Current game score for that frame.</p> </li> <li> <p>duration(ms): Integer. Time elapsed until the human player made a decision. </p> </li> <li> <p>unclipped_reward: Integer. Immediate reward returned by the game engine.</p> </li> <li> <p>action: Integer. See action_enums.txt for the mapping. This is consistent with the Arcade Learning Environment setup.</p> </li> <li> <p>gaze_positions: Null/A list of integers: x0,y0,x1,y1,...,xn,yn. Gaze positions for the current frame. Could be null if no gaze. (0,0) is the top-left corner. x: horizontal axis. y: vertical.</p> </li> </ul> <p> </p> <p>3. action_enums.txt: contains integer to action mapping defined by the Arcade Learning Environment. </p> <p> </p> <p><strong>============================ Citation ============================</strong></p> <p>If you use the Atari-HEAD in your research, we ask that you please cite the following:</p> <p>@misc{zhang2019atarihead,</p> <p> title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},</p> <p> author={Ruohan Zhang and Calen Walshe and Zhuode Liu and Lin Guan and Karl S. Muller and Jake A. Whritner and Luxin Zhang and Mary M. Hayhoe and Dana H. Ballard},</p> <p> year={2019},</p> <p> eprint={1903.06754},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={cs.LG}</p> <p>}</p> <p>Zhang, Ruohan, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, and Dana H. Ballard. "AGIL: Learning attention from human for visuomotor tasks." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 663-679. 2018.</p> <p>@inproceedings{zhang2018agil,</p> <p> title={AGIL: Learning attention from human for visuomotor tasks},</p> <p> author={Zhang, Ruohan and Liu, Zhuode and Zhang, Luxin and Whritner, Jake A and Muller, Karl S and Hayhoe, Mary M and Ballard, Dana H},</p> <p> booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},</p> <p> pages={663--679},</p> <p> year={2018}</p> <p>}</p> <p><br> <br> </p>
EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study
<p>Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords.</p> <p>This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal <em>Acta Psychologica</em> (https://doi.org/10.1016/j.actpsy.2023.103942)</p>
EEG and eye-tracking data from a go/no-go saccadic task based on facial expression cues
<p>Electroencephalographic (EEG) and eye-tracking data from 20 healthy individuals who performed a go/no-go saccadic task based on facial expression cues aimed at studying error monitoring processes.</p> <p> </p> <p>Version 2 includes only the EEG data, but with triggers of correct and erroneous actions. Version 2 has an error that was corrected for version 3.</p> <p> </p> <p>EEG Triggers</p> <table> <tbody> <tr> <td> <p><span>19</span></p> </td> <td> <p><span>Eye tracker starts recording</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Beginning of each trial</span></p> </td> </tr> <tr> <td> <p><span>200</span></p> </td> <td> <p><span>Gap between neutral and instruction</span></p> </td> </tr> <tr> <td> <p><span>99</span></p> </td> <td> <p><span>Fixation cross between instruction and saccade</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Instruction no-go happy</span></p> </td> </tr> <tr> <td> <p><span>21</span></p> </td> <td> <p><span>Target left no-go happy</span></p> </td> </tr> <tr> <td> <p><span>22</span></p> </td> <td> <p><span>Target right no-go happy</span></p> </td> </tr> <tr> <td> <p><span>121</span></p> </td> <td> <p><span>Response period for no-go happy after target left</span></p> </td> </tr> <tr> <td> <p><span>122</span></p> </td> <td> <p><span>Response period for no-go happy after target right</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Instruction no-go sad</span></p> </td> </tr> <tr> <td> <p><span>31</span></p> </td> <td> <p><span>Target left no-go sad</span></p> </td> </tr> <tr> <td> <p><span>32</span></p> </td> <td> <p><span>Target right no-go sad</span></p> </td> </tr> <tr> <td> <p><span>131</span></p> </td> <td> <p><span>Response period for no-go sad after target left</span></p> </td> </tr> <tr> <td> <p><span>132</span></p> </td> <td> <p><span>Response period for no-go sad after target right</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Instruction pro-right</span></p> </td> </tr> <tr> <td> <p><span>42</span></p> </td> <td> <p><span>Target right pro-right</span></p> </td> </tr> <tr> <td> <p><span>142</span></p> </td> <td> <p><span>Response period for pro-right</span></p> </td> </tr> <tr> <td> <p><span>5</span></p> </td> <td> <p><span>Instruction pro-left</span></p> </td> </tr> <tr> <td> <p><span>51</span></p> </td> <td> <p><span>Target left pro-left</span></p> </td> </tr> <tr> <td> <p><span>151</span></p> </td> <td> <p><span>Response period for pro-left</span></p> </td> </tr> <tr> <td> <p><span>6</span></p> </td> <td> <p><span>Instruction anti-left</span></p> </td> </tr> <tr> <td> <p><span>61</span></p> </td> <td> <p><span>Target left anti-left</span></p> </td> </tr> <tr> <td> <p><span>161</span></p> </td> <td> <p><span>Response period for anti-left</span></p> </td> </tr> <tr> <td> <p><span>7</span></p> </td> <td> <p><span>Instruction anti-right</span></p> </td> </tr> <tr> <td> <p><span>72</span></p> </td> <td> <p><span>Target right anti-right</span></p> </td> </tr> <tr> <td> <p><span>172</span></p> </td> <td> <p><span>Response period for anti-right</span></p> </td> </tr> <tr> <td> <p><span>199</span></p> </td> <td> <p><span>Final fixation period (1.5s black screen after last saccade)</span></p> </td> </tr> <tr> <td> <p><span>190</span></p> </td> <td> <p><span>Eye tracker stops recording</span></p> </td> </tr> <tr> <td> <p><span>proCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>proErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>nogoErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a no-go intruction</span></p> </td> </tr> </tbody> </table>
Eye Tracking based Learning Style Identification for Learning Management Systems
<h2>Abstract: </h2> <p>In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.</p> <h3>Research: </h3> <p>As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary: </p> <ul> <li>Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, <em>In 2024 Symposium on Eye Tracking Research and Applications.</em> ETRA.</li> <li>Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany</li> <li>Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In <em>ICERI2023 Proceedings</em> (pp. 5153-5162). IATED.</li> <li>Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In <em>Proceedings of the 5th European Conference on Software Engineering Education</em> (pp. 138-147). ECSEE.</li> </ul> <p>The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. </p> <h3>Experimental Setup:</h3> <p>In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given. <br>In order to reduce the external influence on the experiment, variables such as:</p> <ul> <li>order, number, and presentation of the stimuli,</li> <li>instruction to the participant prior to the experiment,</li> <li>position of the participant in respect to the Eye Tracking equipment,</li> <li>environment such as illuminance and ambient noise for the participant,</li> <li>Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration</li> </ul> <p>were attempted to keep constant and consistent throughout the experiment. </p> <h3>Equipment: </h3> <p>In this study, the <strong>Tobii Pro Fusion</strong> (<a href="https://go.tobii.com/tobii-pro-fusion-user-manual">https://go.tobii.com/tobii-pro-fusion-user-manual</a>) eye tracker is utilized without a chin rest along with the <strong>Tobii IVT filter</strong> for fixation detection and <strong>Tobii Pro Lab</strong> software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.</p> <p>Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design. </p> <h3>Procedure: </h3> <p>Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment. </p> <h3>Stimuli: </h3> <p>The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. <em>illustrations</em> or <em>key words</em>, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.<br>There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs. <br>In detail, the AOI grouping regarding:</p> <ul> <li><em>table of contents and summary contain only a single AOI each,</em></li> <li><em>illustrations</em>, <em>key words</em>, <em>theory</em>, <em>exercise</em>, <em>example</em> and <em>additional material</em> contain three AOIs each,</li> <li><em>supporting text</em> and <em>multiple choice question</em> contain two AOIs each.</li> </ul> <h3>Research data management: </h3> <p>To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire. </p>
Data from: An Easily Compatible Eye Tracking System for Free-moving Small Animals
<p>These datasets are associated with human labled eye tracking datasets in DLC formate and pixel formate from the paper Huang et. al., <em>An Easily Compatible Eye Tracking System for Free-moving Small Animals, </em>2021.<em> </em></p>
An eye tracking dataset for building façade inspection
<p>This dataset contains eye-tracking data of ten participants (students) for building facade inspection of two structures. The participants are in between their mid-twenties to thirties. The sessions were recorded for the preliminary eye tracking study to understand the inspector's reasoning and sense-making for damage assessment. The dataset was collected using Pro Glasses 3 wearable eye tracking system from Tobii Technology and further post-processing was done using Pro Lab software for data analysis purposes.</p>
Unveiling Variations: A Comparative Study of VR Headsets Regarding Eye Tracking Volume, Gaze Accuracy, and Precision
<p>This repository contains the supplementary material to the paper "Unveiling Variations: A Comparative Study of VR Headsets Regarding Eye Tracking Volume, Gaze Accuracy, and Precision".</p> <p>Functions for converting between Fick angles, 3D vectors, and visual angles are authored by Per Baekgaard, available at <em><a href="https://github.com/baekgaard/fickpy">https://github.com/baekgaard/fickpy</a> </em></p> <p> </p> <p>In detail:</p> <p>- Dataset</p> <ul> <li>Analysis scripts</li> </ul> <p>- The Unity application:</p> <ul> <li>testHTCTobiiPro - TobiiPro licence is not included in the upload</li> <li>testViveProEye_sranipal</li> <li>testAndroid <ul> <li>scene: eval_viveFocus3, when building apk, use only Wave as xr provider</li> <li>scene: eval_metaQuestPro, Meta Quest Pro standalone</li> <li>scene: eval_metaQuestPro_pcVr, Meta Quest Pro tethered</li> </ul> </li> </ul>
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 2. Eye-tracking glasses image showing the gaze location during stair ascent
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
ETDD70: Eye-Tracking Dyslexia Dataset
<p>The ETDD70 dataset comprises eye-tracking recordings from 70 Czech participants, equally divided into dyslexic and non-dyslexic readers, all aged 9–10 years. The dataset captures eye movements during three text-reading tasks in Czech: syllable reading (Task 1), meaningful text reading (Task 4), and pseudo-text reading (Task 5).</p> <p>This dataset is the result of the project “Diagnostics of Dyslexia Using Eye-Tracking and Artificial Intelligence” conducted by our research team. The project aims to leverage artificial intelligence tools and advanced technical equipment (eye tracking) to more effectively diagnose dyslexia, one of the most common specific learning disorders, and thereby significantly improve re-education strategies for dyslexic students. The primary goal is to develop models that accurately distinguish between dyslexic and non-dyslexic readers based on eye movement patterns recorded during these tasks.</p> <p>Data collection took place between October 2022 and August 2023, adhering to ethical standards. The project was approved by the Research Ethics Committee of Masaryk University in Brno, Czech Republic.</p> <p>Please contact us if you have any questions or feedback at <a href="mailto:nicol.dostalova@mail.muni.cz">nicol.dostalova@mail.muni.cz</a> or at <a href="mailto:svaricek@phil.muni.cz">svaricek@phil.muni.cz</a>.</p> <p>The ETDD70 dataset is freely available for research purposes.</p> <p><strong>PARTICIPANTS</strong></p> <p>The eye-tracking data were captured from 70 participants: 35 dyslexic and 35 non-dyslexic readers. In all cases, participants are elementary school pupils aged 9-10 years (i.e., 4th grade of elementary school). Recruitment of suitable participants was conducted in cooperation with a psychological counseling center, which facilitated the recruitment of pupils diagnosed with dyslexia. The non-dyslexic readers, who showed no symptoms of dyslexia, were recruited in cooperation with the counseling facilities of selected elementary schools. The dataset was collected from October 2022 to August 2023. The legal representatives of all participants were properly informed about the research procedure and agreed to participate in the study, for which they subsequently received compensation.</p> <p><strong>STIMULI</strong></p> <p>We designed three verbal tasks based on standardized paper-based dyslexia diagnostics used in the Czech Republic. These source texts were transferred to a digital version in a controlled form (e.g., amount of text, font size, line spacing, background color, etc.) for the requirements of eye-tracking measurements.</p> <p>Task called <strong>Syllables</strong> contains 90 syllables arranged in a 9 x 10 matrix. The syllables are commonly encountered in the Czech language. The individual rows of syllables were categorized according to syllable composition (based on linguistic aspects) as follows: open syllables with no meaning, i.e., consonant + vowel (e.g., "ta," "na"), closed syllables with a central vowel bearing a meaning, i.e., consonant + vowel + consonant (e.g., "suk," "mák"), meaningless syllables consisting of two consonants (e.g., "vl," "bz"), a meaningless syllable formed by a cluster of two consonants ending in a vowel (e.g., "tle," "mra"), and finally a meaningful syllable formed by a cluster of three consonants with one vowel in the 3rd position (e.g., "mrak," "vlak"). All syllables were presented in black font, with Times New Roman on a gray background. The objective of the task is to read aloud all syllables from left to right and from top to bottom. A fixation cross was placed in the lower right corner for gaze-contingent task closure—when the participant looks at this cross, the recording is automatically terminated.</p> <p>Task called <strong>MeaningfulText</strong> consists of a passage about a young boy who watches a squirrel from his window. This text is intended for elementary school readers in grades 3 and 4. The stimulus text contains a total of seven text lines with six logical sentences. The text is again written in black-colored font with double line spacing on a grey background and the fixation cross in the lower right corner. The aim of the task is to read the entire text aloud.</p> <p>Task called <strong>PseudoText </strong>comprises fictional, meaningless words. This text has a total of seven lines with 15 artificial sentences. The text formatting, as well as the ending fixation cross, are the same as in Task MeaningfulText. The objective of the task is to read the entire text aloud as smoothly as possible.</p> <p><strong>EYE-TRACKING FEATURES</strong></p> <p>The raw eye-tracking data recorded for each task were further processed to extract event-based characteristics—fixations, saccades, and dozens of derived statistical characteristics. The fixations were detected using the i2mc algorithm (Hessels et al., 2017), as it was specifically designed to be noise-robust for measurements in children. The minimum fixation duration was set to 40 ms. The derived characteristics provide additional information about how participants interact with text. These characteristics are divided into whole-task and region-of-interest (ROI) characteristics. While the whole-task characteristics describe the semantics on the global level of the whole screen, the ROI ones characterize the semantics on the local level of a small rectangular area.</p> <p><strong>Feature-based characteristics for each task:</strong></p> <p><strong>Syllables</strong></p> <p>First fixation duration, average fixation duration, number of fixations, number of fixations and saccades without the incoming/outgoing saccade, number of revisits—incoming saccades hitting this ROI from outside.</p> <p><strong>MeaningfulText</strong>, <strong>PseudoText</strong></p> <p>Whole-task (features extracted from the whole trial): number of regressions, ratio of progressive to regressive saccades, average saccadic amplitude, total reading duration, average fixation duration, number of fixations.</p> <p>ROI (features extracted for separated regions of interest, i.e. lines and words): average fixation duration, number of fixations, number of revisits—incoming saccades hitting this ROI from outside, landing position of the first fixation.</p> <p><strong>AI CLASSIFICATION APPROACH</strong></p> <p>The AI-based methods used for the classification of dyslexia are available here: <a title="https://gitlab.fi.muni.cz/xsedmid/dyslex" href="https://gitlab.fi.muni.cz/xsedmid/dyslex" target="_blank" rel="noreferrer noopener">https://gitlab.fi.muni.cz/xsedmid/dyslex</a></p> <p> </p> <p> </p> <p><strong>CITE THIS DATASET</strong></p> <p>Dostalova, N., Svaricek, R., Sedmidubsky, J., Culemann, W., Sasinka, C., Zezula, P., & Cenek, J. (2024). <em>ETDD70: Eye-tracking Dyslexia Dataset </em>[Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13332134">https://doi.org/10.5281/zenodo.13332134</a></p> <p><strong>CITE THE ASSOCIATED PAPER</strong></p> <p>Sedmidubsky, J., Dostalova, N., Svaricek, R., & Culemann, W. (2024). ETDD70: Eye-tracking dataset for classification of dyslexia using AI-based methods. In <em>Proceedings of the 17th International Conference on Similarity Search and Applications (SISAP)</em> (pp. 1-14). Springer.</p>
COLET: A Dataset for Cognitive workLoad estimation based on Eye-Tracking
<p>Cognitive workload is an important component in performance psychology, ergonomics, and human factors. Unfortunately, benchmarks and publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad state estimation based on Eye-Tracking dataset is presented. Forty-seven (47) individuals' eye movements were monitored as they solved puzzles involving visual search tasks of varying complexity and duration. The authors give an in-depth study of the participants' performance during the experiments while eye and gaze features were derived from low-level eye recorded metrics, and their relationships with the experiment tasks were investigated. Finally, the results from the classification of cognitive workload levels solely based on eye and gaze data, by employing and testing a set of machine learning algorithms are provided. The dataset is made available to the public.</p> <p> </p> <p>Please cite the following work: </p> <p>Ktistakis, E., Skaramagkas, V., Manousos, D., Tachos, N. S., Tripoliti, E., Fotiadis, D. I., & Tsiknakis, M. (2022). Colet: A dataset for cognitive workload estimation based on eye-tracking. <em>Computer Methods and Programs in Biomedicine</em>, 106989. https://doi.org/10.1016/j.cmpb.2022.106989</p>
eSEEd: emotional State Estimation based on Eye-tracking dataset
<p>We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.<br><br>TO USE THIS DATASET PLEASE CITE:<br>Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. <em>Brain Sci.</em> 2023, <em>13</em>, 589. https://doi.org/10.3390/brainsci13040589</p>
Eye Tracking in Robot Control Tasks
<p>This table is part of a systematic review. It contains current work of researchers around the world, who work in the field of eye tracking control for robotic arms. These controls are used for assistive robotics, aiding physically impaired people in everyday life and in shared workspaces.</p><p>This data set can also be found on git: https://github.com/AnkeLinus/EyeTrackingInRobotControlTasks.git </p><p>If you use this table in other publications please cite as stated in the git repository. Also keep an eye open for updates.</p>
Foothold selection during locomotion in uneven terrain: Results from the integration of eye tracking, motion capture, and photogrammetry
Open the record for dataset details and reuse information.
Eye-tracking (EOG) Data
<p>Dataset taken from https://sites.google.com/site/consensusmotifs/</p> <p>Stored on Zenodo as backup for Stumpy Consensus Motif Search</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.