Find research datasets worth reusing

opencc-by-sa-4.0Apr 2024View details →

Audiovisual, Gaze-controlled Auditory Attention Decoding Dataset KU Leuven (AV-GC-AAD)

This dataset is described in detail in the following journal paper [1]: Rotaru, I., Geirnaert, S., Heintz, N., Van de Ryck, I., Bertrand, A., & Francart, T. (2024). What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention. Journal of Neural Engineering, 21(1), 016017. <a href="https://iopscience.iop.org/article/10.1088/1741-2552/ad2214/meta">https://iopscience.iop.org/article/10.1088/1741-2552/ad2214/meta</a> If using this dataset, please cite the original paper above and the current Zenodo repository. Note from the authors: Recent evaluations reveal that various published AAD (Auditory Attention Decoding) algorithms do not achieve significant above-chance performance on this AV-GC-AAD dataset, and in particular on the two gaze-incongruent conditions 'MovingVideo' and 'MovingTargetNoise'). This suggests that previously reported successes may have been largely influenced by eye gaze confounds present in other datasets, which can be exploited as shortcuts by machine learning algorithms. Despite these findings, poor performance on the AV-GC-AAD dataset is often dismissed, with reasons cited such as insufficient training data, high heterogeneity in audiovisual conditions, or the claim that participants were unable to focus their auditory attention due to the complexity of the instructions. To address these concerns, we provide a supplementary technical report (and accompanying code), showcasing results from a simple linear stimulus reconstruction AAD algorithm applied to this dataset. Our findings demonstrate that high AAD accuracy can be achieved within individual conditions, and that the model generalizes across conditions, new subjects, and even across different datasets. <a title="https://doi.org/10.48550/arxiv.2412.01401" href="https://doi.org/10.48550/arXiv.2412.01401" target="_blank" rel="noreferrer noopener">Report</a> | <a title="https://github.com/alexanderbertrandlab/linear-stimulus-reconstruction-aad-av-gc-aad-dataset" href="https://github.com/AlexanderBertrandLab/linear-stimulus-reconstruction-AAD-AV-GC-AAD-dataset" target="_blank" rel="noreferrer noopener">Matlab Code</a> Through this report, we aim to remove any doubts that the AV-GC-AAD dataset's limitations are the primary cause of AAD algorithms failing to exceed chance-level performance. Additionally, this report and its accompanying code offer a simple baseline evaluation procedure, which can serve as a minimal benchmark for testing more advanced AAD algorithms on this dataset. When reporting results on this data set, it is good practice to show performance for each condition separately, since 2 of the 4 conditions still contain gaze shortcuts, which could be exploited by machine learning algorithms.  ________________________________________________________________________________ Dataset description This work was performed at ExpORL, Dept. Neurosciences, KU Leuven and Dept. Electrical Engineering (ESAT), KU Leuven (Belgium), with the goal of investigating and controlling for the effect of gaze during a competing listening task. The full dataset contains EEG and EOG data collected from 16 normal-hearing subjects, during a competing listening task, where the subjects were instructed to focus on one of two competing speech signals. However, subjects 2, 5 and 6 were excluded from the online repository due to not consenting to sharing their data in a public database (cf. signed informed consents approved by KU Leuven Ethical Committee). EEG recordings were conducted in a soundproof, electromagnetically shielded room at ExpORL, KU Leuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. Additionally, the participants' gaze movements were measured via 4 EOG (electrooculography) electrodes placed symmetrically around the eyes.  The audio signals were administered to each subject at 65 dB SPL through a pair of insert phones (Etymotic ER10). In some experimental trials, the video depicting the attended talker was also presented on the screen. The original presented speech and video stimuli (.wav and .mp4 files) are excluded from the dataset due to copyrights. However, the acoustic envelopes of the attended and unattended audio stimuli are calculated and included in the dataset (see below).  The experiments were conducted using custom-made Python scripts. The experimental trials were split into 2 blocks. Each block consisted of the following sequence of conditions: MovingVideo, MovingTargetNoise, NoVisuals, StaticVideo. The auditory task was the same for all conditions: the subjects had to attend to one of the two presented talkers, as indicated by an arrow on the screen. The visual task differed across conditions: <ul> <li>MovingVideo: the subjects had to follow the moving video of the to-be-attended speaker presented on a randomized horizontal trajectory on the screen.</li> <li>MovingTargetNoise: the subjects had to follow a moving cross-hair presented on a randomized horizontal trajectory on the screen.</li> <li>NoVisuals: a black screen was presented and the subjects had to fixate on an imaginary point in the center of the screen while minimizing the eye movements.</li> <li>StaticVideo: the subjects had to fixate the static video of the to-be-attended speaker presented on the same side with the audio stimulus of the attended speaker.</li> </ul> The full description of all experimental conditions can be consulted in [1]. Each trial/condition lasted for 10 minutes, with a spatial switch in attention after 5 minutes (i.e., the  presented speech stimuli  were programmed to swap sides - from L to R or vice versa, such that after the switch the subjects kept listening to the same speaker, but coming from the opposite spatial location). This means that the participant kept attending to the same speaker throughout an entire trial. To keep the subjects motivated, they had to answer one comprehension question related to the attended acoustic stimulus after each trial. For each subject, there is a .mat file containing the following variables: conditionID: the condition ID for each trial  data: the preprocessed EEG and EOG data for each trial (first 64 channels are EEG, last 4 are EOG) fs: the sampling rate of the EEG, EOG and stimuli envelopes initAttention: the initial spatial location of the attended stimulus for each trial metadata: the original metadata (e.g. channel names, triggers) saved in the raw .bdf files for each trial params: the filtering parameters used for each trial randomization: the randomization parameters (e.g. presented stimuli, attention switch times etc.) for each trial stimulus: the precalculated envelopes for the attended and unattended stimuli for each trial subjID: the anonymised ID of the current subject Preprocessing EEG and EOG All the following preprocessing steps were applied per trial. The EEG was initially downsampled using an antialiasing filter from 8192 Hz to 256 Hz. The data was then filtered between 1–40 Hz using a zero-phase Chebyshev filter (type II, with 80 dB attenuation at 10% outside the passband). Finally, downsampling to 128 Hz was performed to speed up computation. Speech envelopes extraction The original speech signals at 44100 Hz were downsampled to 8192 Hz (to match the EEG sampling rate). They were then passed through a gammatone filterbank, which roughly approximates the spectral decomposition as performed by the human auditory system. Per subband, the audio envelopes were extracted, and their dynamic range was compressed using a power-law operation with exponent 0.6 (as proposed in [2]). Each subband was then bandpass-filtered with the same filter used for the EEG data. The resulting subband envelopes were then summed to construct a single broadband envelope. Finally, the envelope signals were downsampled to 128 Hz to match the sampling rate of the preprocessed EEG. Notes <ol> <li>For subjects 1-3, 6 trials corresponding to 3 conditions (MovingVideo, NoVisuals, StaticVideo) were measured.</li> <li>For subjects 4-16, 8 trials corresponding to 4 conditions (MovingVideo, MovingTargetNoise, NoVisuals, StaticVideo) were measured.</li> <li>For subject 14, trial 2 from the StaticVideo condition was not recorded due to some technical problems.</li> <li>In the dataset, 'FixedVideo' is the alias name for the 'StaticVideo' condition described in [1].</li> <li>The EEG/EOG data was not referenced. Before further analysis, rereferencing the data (e.g., to an arbitrary EEG channel, or the common-average of all channels) is necessary to achieve a better common-mode rejection and thus increase the SNR of recorded data. (for details, see https://www.biosemi.com/faq/cms&drl.htm)</li> </ol> References [1] Rotaru, Iustina, et al. "What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention." Journal of Neural Engineering 21.1 (2024): 016017. [2] Biesmans, Wouter, et al. "Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario." IEEE Transactions on neural systems and rehabilitation engineering 25.5 (2016): 402-412.

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Error Related Potentials from Gaze-Based Typesetting

The recording protocol relied on a standard gaze-based keyboard paradigm that was implemented by an eye-tracker attached to a PC monitor. The gazing information, in the form of a densely sampled sequence of x-y coordinates corresponding to the eye trace on the screen, was registered simultaneously with the participant’s brainwaves. The purpose of this experiment was to provide data where patterns in the physiological activity, of either brain or eyes, could be associated with the case of a typo (due to either the inaccuracy of the eye-tracker or a human mistake).

opencc-by-4.0Dec 2017View details →

opencc-by-4.0Oct 2024View details →

Eye image data with gaze labels recorded using custom video-oculography hardware at 120Hz

The repository of eye image data with corresponding gaze labels collected from 40 subjects. The preview contains a collage of random image samples, one per subject.  All recorded subjects gave informed consent under an experimental protocol approved by the Institutional Research Board of Texas State University (approval code 2018044) and their data were anonymized prior to public release. The data were recorded using the custom video-oculography (VOG) desktop hardware setup at 120Hz. The full description of this eye-tracking system's capabilities is provided at https://doi.org/10.48550/arXiv.1904.07361. This VOG set contains recordings of the random oblique saccades task. It is comprised of 174 on-screen fixation targets that densely cover the range of ±20.51° horizontally and ±16.7° vertically (in degrees of visual angle). More detail on the presented stimuli can be found at https://doi.org/10.1145/3379156.3391370. The data were also used in Dmytro Katrychuk's Ph.D. thesis "Generating Realistic Eye Images to Evaluate Photosensor Oculography Eye-Tracking for Portable Headsets" (https://hdl.handle.net/10877/19437); with the release for public use in the upcoming publication "An appearance-based gaze estimation as a benchmark for eye image data generation methods" accepted to MDPI Journal of Applied Sciences.  Each .zip archive represents a recording from one subject, which includes: <ul> <li>Video of the close eye capture in ".avi" format</li> <li>Calibration data in ".xml" format</li> <li>Gaze data in ".tsv" format</li> <li>On-screen target stimulus position in ".tsv" format</li> </ul> The "src.zip" provides a Python script to unpack each ".avi" video recording to a set of ".png" images. The direct playback of ".avi"s may require special codecs and is not supported.  Any additional code will be uploaded to https://github.com/dkatrychuk/psog-eval-diss2023 The authors can be contacted at their corresponding emails: Dmytro Katrychuk - d_k139@txstate.edu; Oleg Komogortsev - ok@txstate.edu.

opencc-by-4.0Feb 2023View details →

Time Series Data of Gaze, Head Pose, Hand Pose, and Object Positions for Object Approaches with a Given Intention

This data set comprises time series data of gaze, head pose, hand pose, and object positions for object approaches with a given intention. The data was captured in the context of the following publication: <ul> <li>Michael Fennel, Serge Garbay, Antonio Zea, Uwe D. Hanebeck, Intention Estimation with Recurrent Neural Networks for Mixed Reality Environments, Proceedings of the 26th International Conference on Information Fusion (Fusion 2023) (under review)</li> </ul> A Microsoft Hololens 2 was used for recording the data at 60 fps under the modalities explained in detail in the above-mentioned paper. The file names are structured as follows: <ul> <li>1st/2nd: <ul> <li>The data with "1st" contains approaches to randomly placed objects on a grid, which are rendered in augmented reality. The user is informed about the object to approach using a visual cue. This corresponds to Section IV-A.</li> <li>The data with "2nd" contains approaches to real objects placed statically in a room. The user is informed about the object to approach using a voice command.</li> </ul> </li> <li>unfiltered: Contains all approaches, including those where the user disrespects the given commands. Filtering is done as described in the paper.</li> <li>train/val/test: The first dataset was split in a 70/20/10 ratio for training, validation, and test.</li> </ul> Each data set contains the following columns. In each approach, 5 objects numbered from i=0 to i=4 are present. <ul> <li>General: <ul> <li>time: in seconds</li> <li>subject: consecutive subject number</li> <li>handedness: left (1), right (0)</li> <li>trial: consecutive trial number per subject</li> <li>target_label: index of the object to approach (0 to 4)</li> </ul> </li> <li>Data in world coordinates: <ul> <li>head_{x,y,z}: head position</li> <li>head_quat_{w,x,y,z}: head orientation quaternion</li> <li>W_gaze_{x,y,z}: gaze direction</li> <li>W_r_hand_{x,y,z}: right hand position</li> <li>W_r_hand_quat_{w,x,y,z}: right hand orientation quaternion</li> <li>W_l_hand_{x,y,z}: left hand position</li> <li>W_l_hand_quat_{w,x,y,z}: left hand orientation quaternion</li> <li>W_object_i_{x,y,z}: position of object i</li> <li>W_object_i_quat {w,x,y,z}: orientation quaternion of object i</li> </ul> </li> <li>Data in egocentric coordinates (head coordinate system). This data is provided for convenience and can be derived from the other data: <ul> <li>gaze_{x,y,z}: gaze direction</li> <li>r_hand_{x,y,z}: right hand position</li> <li>r_hand_quat_{w,x,y,z}: right hand orientation quaternion</li> <li>l_hand_{x,y,z}: left hand position</li> <li>l_hand_quat_{w,x,y,z}: left hand orientation quaternion</li> <li>object_i_{x,y,z}: position of object i</li> <li>object_i_quat {w,x,y,z}: orientation quaternion of object i</li> </ul> </li> </ul> Acknowledgment: This work was supported by the <a href="https://robdekon.de/">ROBDEKON</a> project of the German Federal Ministry of Education and Research.

opencc-by-4.0Jun 2022View details →

NASA GLOBE Cloud GAZE Test Dataset

NASA GLOBE Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement, or CLOUD GAZE is a NASA funded pilot project aimed to help NASA better understand the effect clouds are having on Earth’s climate. The CLOUD GAZE project is a collaboration between two giants of citizen science:<a href="https://www.globe.gov/"> The GLOBE Program</a> and the<a href="https://www.zooniverse.org/"> Zooniverse</a> online platform and is funded through NASA’s Citizen Science for Earth Systems Program. The CLOUD GAZE citizen science project characterizes cloud properties from sky photographs sent in through GLOBE Clouds ground observations. The GLOBE Clouds/CLOUD GAZE team at NASA Langley Research Center extracts cloud properties from sky photographs submitted to the GLOBE Program using the Zooniverse online platform. The team produces datasets from three sources: ground-cloud observations from The GLOBE Program collocated with NASA/NOAA satellite data and the CLOUD GAZE cloud cover and cloud type characterizations. The datasets are for cloud type worldwide investigations and serve as training sets for machine learning. This data is provided as CSV files.   <a href="https://www.globe.gov/documents/16792331/0/Summary+Data+Variables+CLOUD+GAZE_2.0.docx/388b8c8f-e869-148f-31c2-78f2d005f38d?t=1654531372682">NASA GLOBE CLOUD GAZE Data Description</a> The data obtained from the Zooniverse, NASA Langley Research Center (NASA LaRC), and The GLOBE Program are free of charge for use in research, publications, and commercial applications. When data from The Zooniverse, The GLOBE Program, and NASA LaRC are used in a publication, we request this acknowledgment be included, "These data were obtained from the Zooniverse online platform, the GLOBE Program and NASA Langley Research Center." Please include such statements, either where the use of the data or other resource is described, or within the Acknowledgements section of the publication.

opencc-by-4.0Mar 2017View details →

Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.

These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175. Use is free for academic purposes, provided the aforementioned article is appropriately cited. The directory contains the following files stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist. ratings.mat contains the variables arousal - mean arousal rating valence - mean valence rating valence2 - squared mean valence rating (after subtracting midpoint) motivationalValue - mean motivation rating motivaionalValue2 - squared mean motivation rating (after subtracting midpoint) All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive) Experiment 1 fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN. boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively. figure2.m uses these data to computes figure 2 of the article from these data dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. table1.R computes and prints the models for table 1 Experiment 2 fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial. boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally. figure3and4.m generates figures 3 and 4 of the article from these data files. dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively. table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.

opencc-by-4.0Jul 2017View details →

Data + Analyses: "Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance"

This upload contains the experiment scripts (written in Presentation), data, and analyses (performed with MATLAB and SPSS) underlying the publication by Mueller & Fiehler (2017). PloS one. doi:10.1371/journal.pone.0180782

opencc-by-4.0Nov 2019View details →

GAze on TArget Dataset

Dataset reported in the deliverable D3.10 Description GAze on TArget (GATA) dataset is a large-scale annotated gaze dataset, tailored for training deep learning architectures. It was created following the “target search” paradigm where subjects were asked to visually search for a specific object class. Forty-eight different subjects participated in the recording procedure using myGaze capturing sensor. Content The gaze annotations are provided with the JSON file format using the following naming convention. objectID_imageID.json The first part (objectID) denotes the target object class id and the second part (imageID) the COCO image id respectively. Inside the JSON file gaze points annotations, timestamp and x,y coordinates, are included as presented below. [{“Time”:12510830749,”X”:343,”Y”:285}, {“Time”:12510864083,”X”:343,”Y”:285}, {“Time”:12510897394,”X”:343,”Y”:285}, {“Time”:12510930745,”X”:343,”Y”:286}, {“Time”:12510964081,”X”:343,”Y”:287}]

opencc-by-4.0Jan 2024View details →

Unveiling Variations: A Comparative Study of VR Headsets Regarding Eye Tracking Volume, Gaze Accuracy, and Precision

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". Functions for converting between Fick angles, 3D vectors, and visual angles are authored by Per Baekgaard, available at <a href="https://github.com/baekgaard/fickpy">https://github.com/baekgaard/fickpy</a>   In detail: - Dataset <ul> <li>Analysis scripts</li> </ul> - The Unity application: <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>

openapache2.0Jan 2024View details →

Data and code for 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality

This repository contains data and code for Lento B., Segas E., Leconte V., Doat E., Danion F., Péteri R., Benois-Pineau J., de Rugy A. (2024). 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality. doi: It contains a dataset (DBAS22_DataOnline folder) of natural arm movements together with visual and gaze information when reaching objects in a wide reachable space from a precisely controlled, comfortably seated posture. More details could be find in the link publication (see Related identifiers section). The DBAS22_DocOnline folder contains all the documentation files. The MainDataExplained file lists and describes the variables recorded during the experimental phases. In the SummaryOfFiles document, you will find descriptions for all the files within the DBAS22_DataOnline folder, and at the bottom, there is also a file tree that illustrates the file structure. The DBAS22FilesWorkflow document offers an overview of the workflow of experimental file creation during the experiment. The DBAS22_CodeOnline folder contains all the scripts to perform data analysis, listed and described in the files CodeExplanations and DependenciesRelations. The GuideInstall file contains information needed to run the Python code files. The DBAS22_CodeOnline folder also contains the DataPlayer Unity project. Instructions for running the project are provided in the DataPlayerGuide file and SupplementaryVideo2 (see Related identifiers section for more details). The folder DBAS22_DataPlayer_StandAloneApp contains the standalone version of the DataPlayer, which doesn't require any software installation. The DBAS22_VideoOnline folder contains all the videos.

opencc-by-4.0Apr 2024View details →

Eyes on the Narrative: Exploring the Impact of Visual Realism and Audio Presentation on Gaze Behavior in AR Storytelling

This repository contains the supplemental material for the paper "Eyes on the Narrative: Exploring the Impact of Visual Realism and Audio Presentation on Gaze Behavior in AR Storytelling".  <ul> <li>eyetracking_data contains relevant data for plots and analysis</li> <li>GetNumGlances.csv extracts number of glances from raw data</li> <li>lookingAtAvatar.csv is processed data for time looked at avatar</li> <li>numberOfGlances.csv is process data on number of times looked at avatar</li> <li>Order_numberOfglances.Rmd and Order_lookingAtAvatar.Rmd calculates statistics on order effects.</li> <li>Order_Preprocessing.R gets relevant data for analysis of order effects</li> <li>Stats_*.Rmd analysis number of glances, time looked at agent, and correlation.</li> </ul> Further additional material can be found at https://zenodo.org/doi/10.5281/zenodo.10458342

opencc-by-4.0Apr 2018View details →

Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 2. Eye-tracking glasses image showing the gaze location during stair ascent

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).

opencc-by-4.0Apr 2018View details →

Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 1. A simplified representation of the analysed staircase path

opencc-by-4.0Jun 2023View details →

Mapping User Attention: Filtering and Visualizing Relevant UI Components in Screenshots based on Gaze Fixations

These data correspond to the set of problems used for the evaluation of the proposal What Are You Gazing At? An Approach to Use Eye-tracking for Robotic Process Automation. Each problem consists of a set of 10 screenshots with the same look and feel but different data values for those values that can be entered/modify by the user. Each problem has its associated gaze fixation data. In each of the problems there is a key UI element that primarily attracts the attention of the user. The evaluation is based on a set of images which resemble realistic screenshots of activities in the administrative domain. More precisely, 5 different set of screenshots (S) are generated, each of them with a different level of complexity. Complexity is measured in terms of the number of UI elements per screenshot. The sets are: <ul> <li>S1 Mockup-based email view. Represents the activity of viewing an email to check if it contains an attachment. In this case, the key UI element that receives the attention is the attachment inside the email.</li> <li>S2 Mockup-based CRM user details. Represents a user's detail viewing activity within a Client Relationship Management (CRM) platform. The key UI element is the checkbox that indicates if the user has all his invoices paid.</li> <li>S3 Real screenshot email view. Analogous to S1 but with real screenshots. It represents the activity of viewing an e-mail to check if it contains an attachment. In this case, the key UI element to which attention is paid is the attachment contained in the e-mail.</li> <li>S4 Real screenshot CRM user details. Analogous to S2 but with real screenshots. It represents a user's detail viewing activity within a CRM platform. The key UI element is the checkbox indicating whether the user has all their invoices paid.</li> <li>S5 Real screenshot CRM user details. Represents the split-screen display of two applications. On the left side a pdf viewer, showing a covid vaccination certificate. And on the right side a human resources management system (basic recreation of real system for privacy reasons). In this one the detail of the employee to whom the certificate of the left side corresponds is visualized. These screenshots, having two applications, have two key UI elements. In the pdf viewer it is the name of the certificate holder and in the human resources management system it is the name of the employee whose detail view is being displayed. The activity being carried out is the verification that the covid certificate received corresponds to that of an employee.</li> </ul> Two types of filters based on the gaze fixation data are applied to these sets of screenshots: Pre-filtering and Post-filtering, corresponding to applying the filtering before and after detecting UI components in the screenshots, respectively. The structure of the data packages is divided in two folders input and output. The input folder is organized as follows: input/ <ul> <li>screenshots/: corresponds to the screenshots. The sets of screenshots are easily identifiable, they are named following the pattern: SX_screenshot_DDDD.jpeg. Where X indicates to which of the set of screenshots described in the previous list it belongs, and DDDD represents a unique identifier for each screenshot. Each group consists of 10 screenshots, being 50 in total.</li> <li>fixation.json: It is a JSON file that contains a key associated with each of the screenshots. For each screenshot, it contains a "fixation_points" key where information about the fixations that have occurred on the screenshot is stored. Here's an example: <pre><code> "S5_screenshot_0050.jpeg": { "fixation_points": { "334.25#497.166666666667": { "#events": 6, "start_index": 33224, "ms_start": 553962.1467, "ms_end": 554061.9899, "duration": 99.8432000001194, "imotions_dispersion": 0.300325967868111, "last_index": 33229, "dispersion": 14.044275227531914 }, "1258.80769230769#507.576923076923": { "#events": 13, "start_index": 33234, "ms_start": 554128.5427, "ms_end": 554345.3595, ...</code></pre> </li> </ul> The output folder is organized in three subfolders, the first one containing the information of the non-filtered screenshots (i.e. without having applied to them any filtering or processing), and the next two with the information resulting from pre-filtering and post-filtering. output/ <ul> <li>non-filter/ <ul> <li>borders/: screenshots with highlighted borders of all UI components detected in it.</li> <li>components_json/: a collection of JSON files with the same name as the screenshot, containing the "img_shape" key with a list of the screen resolution and the number of layers the image has: [1080, 1920, 3], and the "compos" key with a list of all UI components representing the Screen Object Model.  </li> </ul> </li> <li>pre-filter/ and post-filter/ <ul> <li>borders/: screenshots with the borders of the relevant UI components. In the case of prefiltering, the detection of components is only performed on the parts of the screenshot that have received attention. In postfiltering, the complete screenshot is shown, with only the borders of the relevant UI components highlighted.</li> <li>components_json/: a collection of JSON files with the same name as the screenshot is included, containing the following keys: <ul> <li>"img_shape": A list representing the screen resolution and the number of layers in the image, e.g., [1080, 1920, 3].</li> <li>"compos": A list of all UI components representing the Screen Object Model (SOM). During post-filtering, each UI component is augmented with an additional property called "relevant." If this property is set to true, it indicates that the respective UI component has received attention.</li> </ul> </li> <li>(pre)/(post)filter_attention_maps/: represent the attention maps. In the case of prefiltering, any surface of the screen that has not received attention will be shown in black. In the case of postfiltering, the areas of attention will be shown as red circles, and the UI components whose area intersects with the areas of attention by more than 25% will be shown in yellow.</li> </ul> </li> </ul> In conclusion, the described data package consists of sets of screenshots, accompanied by prefiltering and postfiltering filters using gaze fixation data, enabling the identification of relevant UI components. The organized data packages include input and output folders, where the output folder offers processed screenshots, UI component information, and attention maps. This resource provides valuable insights into user attention and interaction with UI elements on different types of scenarios.

dryad40/100

Data from: Active head movements contribute to spatial updating across gaze shifts

Open the record for dataset details and reuse information.

publicJun 2024View details →

opencc-by-4.0Dec 2016View details →

Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance

The uploaded files contain data as SPSS data files (.sav) and tab delimited (.csv) textfiles as well as the respective variable desriptions as images (.png). In particular, I uploaded 1) the raw data, 2) the means of reach errors which we analyzed by repeated-measures ANOVA, and 3) the means of ellipse sizes on which repeated-measures ANOVA of precision were based. After publication of the article, I also uploaded the reported analyses and corresponding datafiles (plus some more material) here: https://doi.org/10.5281/zenodo.821307

opencc-by-4.0Feb 2014View details →

Gaze-dependent spatial updating of tactile targets in a localization task

The article which was written based on the uploaded dataset can be retrieved here: https://doi.org/10.3389/fpsyg.2014.00066 Abstract: There is concurrent evidence that visual reach targets are represented with respect to gaze. For tactile reach targets, we previously showed that an effector movement leads to a shift from a gaze-independent to a gaze-dependent reference frame. Here we aimed to unravel the influence of effector movement (gaze shift) on the reference frame of tactile stimuli using a spatial localization task (yes/no paradigm). We assessed how gaze direction (fixation left/right) alters the perceived spatial location (point of subjective equality) of sequentially presented tactile standard and visual comparison stimuli while effector movement (gaze fixed/shifted) and stimulus order (vis-tac/tac-vis) were varied. In the fixed-gaze condition, subjects maintained gaze at the fixation site throughout the trial. In the shifted-gaze condition, they foveated the first stimulus, then made a saccade toward the fixation site where they held gaze while the second stimulus appeared. Only when an effector movement occurred after the encoding of the tactile stimulus (shifted-gaze, tac-vis), gaze similarly influenced the perceived location of the tactile and the visual stimulus. In contrast, when gaze was fixed or a gaze shift occurred before encoding of the tactile stimulus, gaze differentially affected the perceived spatial relation of the tactile and the visual stimulus suggesting gaze-dependent coding of only one of the two stimuli. Consistent with previous findings this implies that visual stimuli vary with gaze irrespective of whether gaze is fixed or shifted. However, a gaze-dependent representation of tactile stimuli seems to critically depend on an effector movement (gaze shift) after tactile encoding triggering spatial updating of tactile targets in a gaze-dependent reference frame. Together with our recent findings on tactile reaching, the present results imply similar underlying reference frames for tactile spatial perception and action.

opencc-by-4.0Apr 2024View details →

Female Traumatic Gaze (Julia Pirotte)

References and acknowledgements for an article on Julia Pirotte’s photographs of the immediate aftermath of the Kielce pogrom as a resource for conceptualizing the relationship between trauma and photography, gendered ways of seeing, memory and trauma, body and archive, vision and death, death and the archive, images and history, survival, and destruction.