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47 results for “visual stimuli”
Dataset for Human visual gamma for color stimuli
<p>This repository contains per-trial data and R analysis code reported in Stauch, B., Peter, A., Ehrlich, I., Nolte, Z., and Fries, P. (2022), <em>Human visual gamma for color stimuli.</em> eLife 11:e75897. doi: <a href="https://doi.org/10.7554/eLife.75897"> 10.7554/eLife.75897</a>. If you want to have a look at the full analysis outcomes, start with analysis_notebook.html. The underlying code is in analysis_notebook.rmd.<br> </p> <p>Additionally, Matlab code that was used to extract per-trial data from the raw data is provided as preprocessingCode.zip.</p>
Visual and Auditory vection stimuli reduce motion sickness
<p>This is the raw data and the full data set from all of our participants included in the analysis for this experiment. </p> <p>The raw data represents the data recorded throughout the experience. Motion Sickness scores, performance on reading task, performance on attention task. </p> <p>While the full data set (Final1) additionally includes questionnaire data (SSQ, NASA TLX, IPQ,...) as well as demographic data of the participants. </p>
SSVEP database elicited by four visual stimuli types
<ul> <li>The database consists of 108 electroencephalographic files from 27 participants performing a 5-target selection task. </li> <li>Each participant performed one experimental session.</li> <li>All datasets were collected on channels PO7, PO3, POz, PO4, PO8, O1, Oz, and O2, according to the 10–20 EEG electrode placement standard.</li> <li>For the visual stimuli, we consider the On-Off and Checkerboard patterns with luminance modulated by rectangular and sinusoidal functions, resulting in a total of four types of visual stimuli: Checkerboard pattern with the rectangular modulated signal (Sxx-C.txt); Checkerboard pattern with sinusoidal modulated signal (Sxx-mC.txt); On-Off pattern with sinusoidal modulated signal (Sxx-mOO.txt) and On-Off pattern with rectangular modulated signal (Sxx-OO.txt), where "Sxx" represents the subject number and 01 <= xx <= 27.</li> <li>In each file columns 1 to 8 correspond to EEG data and column 9 corresponds to the marks channel.</li> <li>Each phase of the experiment block is identified with a marker.</li> <li>The phases of one experiment trial are Fixation(201), Target Presentation(202), Preparation(203), Stimulation(101-105), and Rest(200).</li> <li>Marker numbers 101, 102, 103, 104, and 105, encodes de target frequency applied during the "Stimulation" stage in a trial. They are associated with the stimulation frequencies as follows: 101 - 24 Hz; 102 - 20 Hz; 103 - 15 Hz; 104 - 10.909 Hz and, 105 - 8.57 Hz</li> <li>Files can be easily accessible with EEG-dedicated MATLAB toolboxes, such as Fieldtrip and EEGLAB.</li> </ul>
Assesment of the visual stimuli properties in P300 paradigm
Open the record for dataset details and reuse information.
"The Veiled Virgin illustrates visual segmentation of shape by cause": Stimuli and Experimental Data
<p>Stimuli and raw experimental data from the experiments reported in PNAS article "The Veiled Virgin illustrates visual segmentation of shape by cause"</p>
Tadpoles rely on mechanosensory stimuli for communication when visual capabilities are poor
<p>The ways in which animals sense the world changes throughout development. For example, young of many species have limited visual capabilities, but still make social decisions, likely based on information gathered through other sensory modalities. Poison frog tadpoles display complex social behaviors that have been suggested to rely on vision despite a century of research indicating tadpoles have poorly-developed visual systems relative to adults. Alternatively, other sensory modalities, such as the lateral line system, are functional at hatching in frogs and may guide social decisions while other sensory systems mature. Here, we examined development of the mechanosensory lateral line and visual systems in tadpoles of the mimic poison frog (<em>Ranitomeya imitator)</em> that use vibrational begging displays to stimulate egg feeding from their mothers. <em>We found that tadpoles hatch with a fully developed lateral line system. While begging behavior increases with development, ablating the lateral line system inhibited begging in pre-metamorphic tadpoles, but not in metamorphic tadpoles.</em> We also found that the increase in begging and decrease in reliance on the lateral line co-occurs with increased retinal neural activity and gene expression associated with eye development. Using the neural tracer neurobiotin, we found that axonal innervations from the eye to the brain proliferate during metamorphosis, with little retinotectal connections in recently-hatched tadpoles. We then tested visual function in a phototaxis assay and found tadpoles prefer darker environments. The strength of this preference increased with developmental stage, but eyes were not required for this behavior, possibly indicating a role for the pineal gland. Together, these data suggest that tadpoles rely on different sensory modalities for social interactions across development and that the development of sensory systems in socially complex poison frog tadpoles is similar to that of other frog species.</p>
Figure 4. (a) Therapy player software screen, where a) is the stimuli time, b) is the total therapy time, c) is the file path, d) displays the numeric values of each sequence of the therapy, e) shows the current value, and f) shows the current lag angle for zenith and azimuth values; (b) USB mechanism for conversion, where a) USB-UART converter, and b) USB-Zigbee converter.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>Where tt time expended by the servomotors to point the laser to a given position and execute<br> a laser beam sequence; tspin is the time that a servomotor needs to spin one degree; ttol is a given the<br> tolerance time; θservo is the addition of degrees that both servos in a laser driver need to spin point<br> the laser in a given position; tstimuli is the time expended in execute a laser beam, between 250 and<br> 605 ms (Weiskrantz et al., 1991); T is the total time of all repetitions in a therapy, suggested<br> between 20 and 60 minutes and N is the number of repetitions in a therapy.</p>
Figure 3. (Top): Illustration of the therapy selection main menu. This enables the user to select one of three options for the therapy. Stimuli sequence selectors; (Bottom): (a) Short distance – complete visual field; (b) Short distance – macular; (c) Middle-long distance.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>distance therapies for the prescribed time suggested by the ophthalmologist.<br> Note that the complete visual field therapy stimulates different parts in the entire visual field<br> whereas macular therapy stimulate only a small part of the visual field, only the first 10° of vision<br> range. In contrast, middle-long distance therapies are not developed inside the device; instead the<br> patient must sit watching a wall, where the stimuli will be presented. Figure 3 (Bottom) shows the<br> sequence selectors for the three different cases. The therapist will choose a desired number of<br> sequences according to the results of the examination to each patient; hence it is completely patient<br> dependent.<br> Once the therapist finishes the particular design of the stimuli sequence, the software<br> automatically displays a window where he can save the customized patient-specific details for future<br> use as a text file.</p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 4: Visual stimuli
<p>In order to test the developed features, the SSVEP datasets recorded in (Nakanishi et al. 2014) is used. Flickering boxes had been presented on 24-inch LCD monitor with a refresh rate of 75Hz. 32 visual stimuli had been generated with 8 different frequencies (8 Hz, 9 Hz, …, 15 Hz) and 4 different phases (0<sup>o </sup>, 90<sup>o</sup> , 180<sup>o</sup> , 270<sup>o</sup> ) as shown in Figure 4. Thirteen healthy adults had participated in the experiments. EEG data had been recorded by 16 electrodes (FPz, F3, F4, Fz, C<sub>z</sub>, P1, P2, P<sub>z</sub>, PO3, PO4, PO7, PO8, PO<sub>z</sub>, O1, O2 and Oz). The sampling rate had been 512 Hz. The datasets are grouped into 4 groups. The 0-degree stimuli formed the 1st group, the 90- degree stimuli the 2<sup>nd</sup> group, the 180-degree stimuli the 3<sup>rd</sup> group and the 270-degree stimuli the 4<sup>th</sup> group. Thus, it is made possible to test the developed features in more datasets. </p>
Visual stimuli used in fMRI study on top-down feedback across cortical depths
<p>These videos contain samples of visual stimuli used in an fMRI study on top-down feedback across cortical depths in human early visual cortex [in preparation]. There is one video sample for each of the three experimental conditions in the main experiment: ‘Pac-Man dynamic’, ‘Pac-Man static’, and ‘control dynamic’. In addition, there are videos of stimuli used in a control experiment in which the shape of the stimulus (‘Pac-Man’ or square) and the background (texture or uniform) was varied. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the rest blocks surrounding the stimulus presentations were much longer. The stimulus design of the main experiment is adapted from Akin et al. (2014).</p> <p>Akin, B., Ozdem, C., Eroglu, S., Keskin, D. T., Fang, F., Doerschner, K., Kersten, D., Boyaci, H. (2014). Attention modulates neuronal correlates of interhemispheric integration and global motion perception. Journal of Vision, 14(12). https://doi.org/10.1167/14.12.30</p>
Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex
<p class="MsoNormal"><span>In everyday life, we integrate visual and auditory information in routine tasks such as navigation and communication. While concurrent sound can improve visual perception, the neuronal <span>correlates of audiovisual integration are not fully understood. Specifically, it remains unclear whether neuronal firing patters in the primary visual cortex (V1) of awake animals demonstrate similar sound-induced improvement in visual discriminability. Furthermore, presentation of sound is associated with movement in the subjects, but little is understood about whether and how sound-associated movement affects audiovisual integration in V1. Here, we investigated how sound and movement interact </span>to modulate V1 visual responses in awake, head-fixed mice and whether this interaction improves neuronal encoding of the visual stimulus. We presented visual drifting gratings with and without simultaneous auditory white noise to awake mice while recording mouse movement and V1 neuronal activity. Sound modulated activity of 80% of light-responsive neurons, with 95% of neurons increasing activity when the auditory stimulus was present. A generalized linear model revealed that sound and movement had distinct and complementary effects of the neuronal visual responses. Furthermore, decoding of the visual stimulus from the neuronal activity was improved with sound, an effect that persisted even when controlling for movement. These results demonstrate that sound and movement modulate visual responses in complementary ways, improving neuronal representation of the visual stimulus. This study clarifies the role of movement as a potential confound in neuronal audiovisual responses and expands our knowledge of how multimodal processing is mediated at a neuronal level in the awake brain.</span></p> <p></p>
Tadpoles rely on mechanosensory stimuli for communication when visual capabilities are poor
Open the record for dataset details and reuse information.
Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex
Open the record for dataset details and reuse information.
Visual sensitivity for luminance and chromatic stimuli during the execution of smooth pursuit and saccadic eye movements
<p>Dataset relative to the following publication:</p> <p>Braun, D. I., Schütz, A. C., & Gegenfurtner, K. R. (2017). Visual sensitivity for luminance and chromatic stimuli during the execution of smooth pursuit and saccadic eye movements. Vision Research</p>
Data, stimuli, and analyses for "High-level aftereffects reveal the role of statistical features in visual shape encoding"
<h2><strong>Data and code share.</strong></h2><p>This record contains data and code (written in MATLAB) to reproduce the results shown in:</p><p>Morgenstern, Y. , Storrs, K., R., Schmidt, F., Hartmann, F., Tiedemann, H., Tiedemann, H, ., Wagemans, J., & Fleming, R. (<i>in press</i>) . High-level aftereffects reveal the role of statistical features in visual shape coding. Current Biology</p><p>Below is a summary of shared scripts that load data, run the analysis (including options for fitting model parameters or loading pre-computed fitted parameters), and plotting the results.</p><h3><strong>Figure 1</strong></h3><ul><li><i>Fig1C_shapespace.m</i>: draw shape space (as in Figure 1C)</li><li><i>Fig1EFG_plotpsychometricdata.m</i>: fit psychometric model to pooled data and plot (as in Figure 1EFG)</li><li><i>getExptShapesHbias.m</i>: saves a data structure (which we call 'package') with adaptor, test, and human biases from experiment 1. (Used to fit models; e.g., see <i>fitGabPyr2Hbais.m</i> or <i>fitTAEGANfit2Hbais.m</i>)</li></ul><h3><strong>Figure 2 and 3A</strong></h3><ul><li><i>fig3A_modeval_expt1.m</i>: generate figure that evaluates models in Figure 3A on how well they predict aftereffects in Experiment 1. (script located in the 'Figures 2 and 3A/models' directory).</li></ul><p>Code to fit the models, and figures that show examples of model predictions are in the model directories, and summarized below:</p><h4>Model: <strong>GabPyrAE</strong> </h4><ul><li><i><strong>note: </strong></i>To run GabPyr, you will likely need to recompile the .mex files in 'matlabPyrTools/mex'. Then move the recompiled files into the 'matlabPyrToos' directory</li><li><i>fig2B_GabPyrAEVisFigs.m</i>: produce GabPyrAE model images for example adaptor and test image ( as in Figure 2B )</li><li><i>figS2BC_GabPyrAEExp</i>.m: get GabPyrAE model responses to simulated tilt aftereffect experiment using anisotropic noise, and plot model responses as in Figures S2BC.</li><li><i>getGabPyrAEMod.m: </i>given an adaptor and test image, this function produces the unfit GabPyrAE prediction</li><li><i>fitGabPyr2Hbias</i>.m: fit GabPyrAE model to best predict human baises in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitGabPyrNormMod2Stims</i>.m</li><li><i>evalGabPyrAE_fitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalGabPyrAE_unfitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>TAE</strong></h4><ul><li><i>fig2CD_TAEmod.m</i>: produce TAE model images for example adaptor and test image (as in Figure 2CD)</li><li><i>getTAEModonShape.m: </i>given an adaptor and test image, this function produces TAE Original prediction. Input to function is adaptor and test shapes, and TAE model parameters alpha and sigma.</li><li><i>getTAEGAN_spwt_onShape.m: </i>given an adaptor and test image, this function produces TAEGAN prediction. Input to function is adaptor and test shapes, and TAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getTAE_spwt_onShape_nn.m: </i>given an adaptor and test image, this function produces TAE nearest neighbour prediction. Input to function is adaptor and test shapes, and TAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitTAEGAN2Hbias</i>.m: fit TAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAEGANMod2Stims</i>.m</li><li><i>fitTAENN2Hbias</i>.m: fit TAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAENNMod2Stims</i>.m.</li><li><i>evalTAEGAN_fitmod_aic.m</i>: evaluate TAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAENN_fitmod_aic.m</i>: evaluate TAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAE_unfitmod_aic.m</i>: evaluate TAE Original model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>PSAE</strong></h4><ul><li><i>fig2EF_PSAEmod.m</i>: produce PSAE model images for example adaptor and test image</li><li><i>getPos_spwt_ShiftononShape_io.m: </i>given an adaptor and test image, this function produces PSAEGAN prediction. Input to function is adaptor and test shapes, and PSAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getPos_spwt_ShiftonShape_io_nn.m: </i>given an adaptor and test image, this function produces PSAE nearest neighbour prediction. Input to function is adaptor and test shapes, and PSAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitPSAEGAN2Hbias</i>.m: fit PSAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAEGANMod2Stims</i>.m</li><li><i>fitPSAENN2Hbias</i>.m: fit PSAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAENNMod2Stims</i>.m.</li><li><i>evalPSAEGAN_fitmod_aic.m</i>: evaluate PSAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalPSAENN_fitmod_aic.m</i>: evaluate PSAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li></ul><h4>Model: <strong>ShapeComp </strong>and <strong>No Adaptation</strong></h4><ul><li><i>eval_ShapeComp _aic.m</i>: evaluate ShapeComp 1 parameter fitted model on how well it predicts human biases from experiment 1.</li><li><i>eval_NoAdaptation _aic.m</i>: evaluate model that predicts no adaptation on how well it predicts human biases from experiment 1.</li></ul><h3><strong>Figure 3BC</strong></h3><ul><li><i>fig3BC_Experiment2.m</i>: load, analyze, and plot experiment 2 data (as in Figure 3B and C).</li><li><i>figS4_Expt2_stimuli.m</i>: show adaptors (in black) and test shapes for ShapeComp (purple), PSAE fit GAN (green), and no adaptation model (white) (as in Figure S4)</li></ul><p> </p>
LEIRO. The Leipzig kit for testing irony comprehension - Visual Stimuli (examples)
<p>Examples of visual stimuli for LEIRO - The Leipzig kit for testing irony comprehension.</p> <p>Drawings were created by <a href="https://simonefass.de/" target="_blank" rel="noopener">Simone Fass</a> (illustrator) under <a href="https://creativecommons.org/licenses/by-sa/4.0/deed.en" target="_blank" rel="noopener">CC-BY SA Licence</a>. Picture elements were remixed by the authors for presentation in the study.</p>
Visual stimuli elicit feedforward and feedback waves in mouse cortex (data and code)
<p>See the readme file for details of the information contained therein.</p> <p>There are also separate readme files for publicly available github repositories from Lyle Muller and the circular statistics toolbox (both for matlab). </p> <p>This dataset includes both the raw data and the analysis code used to process them in Aggarwal et al, Nature Communications, 2022. </p>
Visual stimuli used in fMRI experiment on processing of real and illusory surfaces
<p>These videos contain samples of visual stimuli used in an fMRI experiment on the processing of real and illusory surfaces in human early visual cortex [the experiments are part of my PhD thesis, in preparation]. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the duration of rest blocks was much longer.</p>
Investigating the Impact of Visual Stimuli on the Auditory Selective Attention in a VR Classroom
<h2>General</h2> <p>The audio-visual Auditory Selective Attention - visual Priming (avASAvisPrim) project serves to investigate the impact of visual stimuli on auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data as well as the collected data and R scripts used for evaluation.</p> <p><strong>The dataset contains:</strong></p> <p> Unity project for audiovisual display and the experiment structure<br> Matlab code for experiment preparation and HpFT measurement<br> Data collected in the experiment (experiment performance)<br> R code for data evaluation</p> <h2>Experiment preparation using Matlab</h2> <p>The code and software used to prepare the experiment is provided in the folder "matlab_avASAvisPrim".</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox). A developmental version of Virtual acoustics (VA) 2021a (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p><strong>Software requirements:</strong></p> <p> Matlab 2020a or higher<br> ITA Toolbox for Matlab installed</p> <h2><br>Experiment conduction in Unity</h2> <p>The Unity project is provided in the folder "unity_avASAvisPrim".</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>The acoustic stimuli for the task are taken from Loh and Fels 2023 "ChildASA dataset: Speech and Noise Material fpr Child-appropriate Paradigms on Auditory Selective Attention" https://doi.org/10.18154/RWTH-2023-00740. </p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br>The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/ and https://git.rwth-aachen.de/ita/vaunity_package).</p> <p><strong>Software requirements:</strong></p> <p> Unity 2019.4.21.f1.<br> SteamVR 1.19.7<br> Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</p> <h2><br>Data evaluation</h2> <p>The collected data is provided in the folder "dataEvaluation_avASAvisPrim". This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates). R code for the evaluation of the head tracking data and the questionnaires is provided.</p>
EFFECT OF VISUAL STIMULI ON THE JUMPING ABILITY OF AMATEUR SOCCER PLAYERS DATA
<p>Data from the study "EFFECT OF VISUAL STIMULI ON THE JUMPING ABILITY OF AMATEUR SOCCER PLAYERS"</p>
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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.