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94 results for “Haptics”
Haptic three-dimensional curved surface exploration fMRI dataset
Open the record for dataset details and reuse information.
Data to "Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G.^, Schepko, M.^, Klein, L. K., Paulun, V. C., and Fleming, R. W. (2021) Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback. Front. Neurosci. 14:591898.<br> doi: 10.3389/fnins.2020.591898</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2020.08.11.246173</p>
Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Experimental study dataset: "Enhancing Touch Sensibility by Sensory Retraining in a Sensory Discrimination Task via Haptic Rendering"
<p>Experimental study dataset: "Enhancing Touch Sensibility by Sensory Retraining in a Sensory Discrimination Task via Haptic Rendering."</p> <p>This research was supported by the Swiss National Science Foundation through the grant PP00P2 163800. This work was also supported by SENACYT and IFARHU, the Panamanian Government.</p> <p>Files and data to upload:</p> <ol> <li>Description of variables </li> <li>Continue robot data </li> <li>Discontinue robot data </li> <li>Questionnaire data </li> </ol> <p> </p>
Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise"
<p>Data relating to the manuscript "Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise". This includes the experiment dataset (in file experiment_dataset.csv) as well as the MATLAB functions used for the development of the simulation model (with main function main_delay.m)</p>
Raw data for the book chapter "Review of Haptic and Computerized (Simulation) Games on Climate Change"
<p>Raw data used for the book chapter "Gerber, A., Ulrich, M., Wäger, P. (2021). Review of Haptic and Computerized (Simulation) Games on Climate Change. In: Wardaszko, M., Meijer, S., Lukosch, H., Kanegae, H., Kriz, W.C., Grzybowska-Brzezińska, M. (eds) Simulation Gaming Through Times and Disciplines. ISAGA 2019. Lecture Notes in Computer Science(), vol 11988. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-72132-9_24">https://doi.org/10.1007/978-3-030-72132-9_24</a>"</p> <p>The documents include the raw data (both as .csv and .xlsx files with the same content), as well as the publication (.pdf file). The data collection process and the data itself are described in the publication. The data is published as "supplementary material" on the publisher's homepage.</p>
User Study Data for "Perception of Ultrasound Haptic Focal Point Motion"
<p>Data from two experiments about the perception of ultrasound haptic feedback.</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Elastic Wave Simulations (Open Scanning Paths)
<p><strong>Elastic Wave Simulations - Open Scanning Paths</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" <a href="http://www.science.org/doi/10.1126/sciadv.adf2037">(Reardon et al., 2023)</a>. If you use these simulated data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>) and the software package k-Wave (DOI: 10.1109/ULTSYM.2014.0037).</p> <p>This dataset contains the normal shear surface velocity in a cylindrical slab of tissue-like material excited by an acoustic source with a Gaussian spatial profile simulated via a pseudo-spectral numerical method. The data is provided as .mat files. The files are separated by the type of scanning path, the scanning speed of the acoustic source, and the parameters of the scanning path. Details of the simulation parameters can be found in our publication.</p> <p><strong>Line Paths</strong> - The acoustic source scanned along a linear trajectory at speeds ranging from 2 m/s to 12 m/s (scanning speed is indicated in the filename).</p> <p><strong>Zigzag Paths</strong> - The acoustic source scanned along a zigzag path on the surface of the simulated medium with x-axis scanning speed <em>v<sub>x</sub></em> = 3, 4, 5, 6 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, <em>v<sub>y</sub></em>, of +-2.5 m/s yielding a zigzag path (2 cm path width). The x-axis scanning speed is designated in the filename.</p> <p><strong>Letter Paths</strong> - The acoustic source scanned the a trajectory in the shape of the letter "Z." Scanning speeds ranged from 2 m/s to 12 m/s (scanning speed is designated in the filename).</p> <p><strong>Focus Control Rate</strong> - The acoustic source scanned along a linear trajectory at 7 m/s but at different focus control sample rates <em>f<sub>c</sub></em>. These paths amount to a courser sampling of the linear trajectory. In lieu of updating the location of the acoustic source at each timepoint in the simulation, we specified a rate at which the location of the acoustic source would be updated. We set <em>f<sub>c</sub></em> to approximately 0.7, 1.4, and 4.2 kHz (designated at the end of the filename as VeryCoarse, Coarse, and Fine, respectively). (Compare with Line_07, which has the finest path sampling and an *f<sub>c</sub>* of approximately 200 kHz.)</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>surfaceData</strong> (NxNxM) - 3D array containing the normal shear velocity of the simulated medium (in m/s) on a NxN Cartesian grid of locations at M timepoints. The simulated tissue medium was cylindrical, so locations outside the circular top surface are NaN.</p> <p><strong>sourceSignals</strong> (NxNxQ) - 3D array containing the acoustic source distribution on the NxN Cartesian grid of locations used to excite the surface of the simulated tissue medium for Q timepoints.</p> <p><strong>sourceEnvelope</strong> (Qx1) - Vector containing the amplitude envelope that was applied to sourceSignals at each timestep Q</p> <p><strong>dt</strong> - The time between adjacent timepoints in seconds (i.e. fs = 1/dt)</p> <p><strong>dx/dy</strong> - The distance between adjacent grid locations in the x/y direction of the Cartesian grid (in m)</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound
<p>This repository contains links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these data please cite our publication found here: <a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>.</p> <p> </p> <p><strong>Abstract From Manuscript</strong></p> <p>Emerging holographic haptic interfaces focus ultrasound in air to enable their users to touch, feel, and manipulate three-dimensional virtual objects. However, current holographic haptic systems furnish tactile sensations that are diffuse and faint, with apparent spatial resolutions that are far coarser than would be theoretically predicted from acoustic focusing. Here, we show how the effective spatial resolution and dynamic range of holographic haptic displays are determined by ultrasound-driven elastic wave transport in soft tissues. Using time-resolved optical imaging and numerical simulations, we show that ultrasound-based holographic displays excite shear shock wave patterns in the skin. The spatial dimensions of these wave patterns can exceed nominal focal dimensions by more than an order of magnitude. Analyses of data from behavioral and vibrometry experiments indicate that shock formation diminishes perceptual acuity. For holographic haptic displays to attain their potential, techniques for circumventing shock wave artifacts, or for exploiting these phenomena, are needed.</p> <p> </p> <p><strong>Dataset Description</strong></p> <p>This dataset comprises surface velocity responses of materials to ultrasound-based holographic haptic displays. The dataset is split into three parts: numerical simulations on a tissue-like material, experimental measurements on a tissue phantom, and in vivo experimental measurements on a human hand. For details on our numerical and experimental procedure, please see our publication.</p> <p> </p> <p><strong>Elastic Wave Simulations</strong></p> <p>The elastic wave simulation dataset contains the surface velocity response of a tissue-like material to an acoustic source scanned across the medium surface and is split into two parts: closed scanning paths (circle and square paths) and open scanning paths (line, zigzag, and letter). These datasets can be found at the following DOIs: 10.5281/zenodo.7686542 and 10.5281/zenodo.7686550.</p> <p> </p> <p><strong>Vibrometry Measurements with Elastomer Plate</strong></p> <p>Data on our tissue phantom was captured via laser doppler vibrometer. This dataset contains the tissue phantom response to focused ultrasound scanned across the tissue phantom surface along linear and zigzag paths. This dataset can be found at the following DOI: 10.5281/zenodo.7686555.</p> <p> </p> <p><strong>Human Hand: Wave Patterns and Perception</strong></p> <p>In vivo measurements on the human hand were captured via laser doppler vibrometer. This dataset contains the skin response to focused ultrasound scanned in a zigzag path from the wrist to the distal end of digit 2 (and vice-versa) of a single participant. We also captured a behavioral dataset that assessed tactile motion direction discrimination. Participants reported the direction of scanning via a two-alternative forced-choice task. Written, informed consent was gathered from all participants in this study, and the protocol was approved by the human subjects committee of our institution. This dataset can be found at the following DOI: 10.5281/zenodo.7686561.</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Elastic Wave Simulations (Closed Scanning Paths)
<p><strong>Elastic Wave Simulations - Closed Scanning Paths</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these simulated data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>) and the software package k-Wave (DOI: 10.1109/ULTSYM.2014.0037).</p> <p>This dataset contains the normal shear surface velocity in a cylindrical slab of tissue-like material excited by an acoustic source with a Gaussian spatial profile simulated via a pseudo-spectral numerical method. The data is provided as .mat files. The files are separated by the type of scanning path, the scanning speed of the acoustic source, and the parameters of the scanning path. Details of the simulation parameters can be found in our publication.</p> <p><strong>Circle Paths</strong> - The acoustic source was scanned at a constant linear speed along a circular trajectories with two different diameters - 1 cm and 3 cm (indicated in the filename) and for at least 2 pattern repetitions. The linear scanning speed ranged from 2 to 20 m/s and is designated in the filename.</p> <p><strong>Square Paths</strong> - The acoustic source was scanned at a constant speed along square trajectories with two different edge lengths - 1 cm and 3 cm (indicated in the filename) and for at least 2 pattern repetitions. The scan speed ranged from 2 m/s to 10 m/s and is designated in the filename.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>surfaceData</strong> (NxNxM) - 3D array containing the normal shear velocity of the simulated medium (in m/s) on a NxN Cartesian grid of locations at M timepoints. The simulated tissue medium was cylindrical, so locations outside the circular top surface are NaN</p> <p><strong>sourceSignals</strong> (NxNxQ) - 3D array containing the acoustic source distribution on the NxN Cartesian grid of locations used to excite the surface of the simulated tissue medium for Q timepoints</p> <p><strong>sourceEnvelope</strong> (Qx1) - Vector containing the amplitude envelope that was applied to sourceSignals at each timestep</p> <p><strong>nCycles</strong> - Number of pattern repetitions</p> <p><strong>dt</strong> - The time between adjacent timepoints in seconds (i.e. fs = 1/dt)</p> <p><strong>dx/dy</strong> - The distance between adjacent grid locations in the x/y direction of the Cartesian grid (in m)</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Human Hand: Wave Patterns and Perception
<p><strong>Human Hand: Wave Patterns and Perception</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>).</p> <p>This dataset contains the in vivo response of a single participant's hand to focused ultrasound (UHEV1, Ultrahaptics) scanned in a zigzag path from the wrist to the distal end of digit 2 (and vice-versa). The data is provided as .mat files. The files are separated via longitudinal scanning speed, <em>v<sub>l</sub></em> = 1, 2, 4, 7, 11 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, <em>v<sub>mod</sub></em> of +-2.5 m/s yielding a zigzag path (2 cm path width). The longitudinal speed is designated in the filename. The direction of scanning - either from the wrist to the distal end of digit 2 (Distal direction) or from the distal end of digit 2 to the wrist (Proximal direction) - is also designated in the filename. Written, informed consent was gathered from the participant in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication.</p> <p>IMPORTANT - The data is the unprocessed output from a laser doppler vibrometer (PSV-500, Polytec). The data is NOT time-aligned and must be reconstructed using the reference signal and the map of the measurement locations.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>y</strong> (NxMx2) - 3D array containing the skin velocity normal to the laser doppler vibrometer (in m/s) at N measurement locations for M timepoints and 2 repetitions</p> <p><strong>ref</strong> (NxMx2) - 3D array containing a reference voltage signal taken from the ultrasound phased array. The beginning of the reference signal can be used to time-align each of the measurements and repetitions</p> <p><strong>fs</strong> - Laser doppler vibrometer sampling rate (in Hz)</p> <p><strong>measurementLocations</strong> (Nx3) - 3D locations on the hand (x,y,z; in m) for each of N measurement locations<br> <br> </p> <p> </p> <p><strong>BehavioralDataset.zip</strong></p> <p>Contains the responses from three different perception experiments on tactile motion direction discrimination. The experiments are provided in three separate files; the results are provided as a MATLAB table. Written, informed consent was gathered from all participants in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication.</p> <p>In the first experiment, SSW_PrimaryDataset.mat, participants (N=12) identified the direction of the focused ultrasound as either moving from the wrist to the end of digit 2 (Distal direction) or from the end of digit 2 to the wrist (Proximal direction).</p> <p>The second experiment, SSW_SecondaryDataset-Zigzag.mat, was nearly identical to the first experiment, except we cyclically repeated the stimuli such that the total integrated time in which the stimulus was applied to the skin was approximately constant between all of the different scan speeds. The participants (N=3) identified the motion direction of the focused ultrasound as either "Distal" or "Proximal" under two conditions - one in which there was no delay between our cyclical repeats (No Delay condition) and a second in which there was a 500 ms time delay between subsequent repetitions (With Delay condition).</p> <p>The third file, SSW_SecondaryDataset-Circle.mat, presents the pilot results (N=1) of a similar tactile motion experiment, except with circular trajectories (radius 2.8 cm) drawn on the palm of the hand in either a clockwise or counterclockwise direction. The stimuli were also repeated cyclically (with and without delay between repetitions), similar to experiment two.</p> <p> </p> <p> </p> <p><strong>Table Fields - SSW_PrimaryDataset.mat</strong></p> <p><strong>Participant</strong> - Participant label</p> <p><strong>Speed</strong> - Longitudinal speed, *v<sub>l</sub>*, of the focused ultrasound stimulus (in m/s)</p> <p><strong>Response</strong> - Participant response as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>Direction</strong> - True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>isCorrect</strong> - Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition</strong> - Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Distal" or "Proximal"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Distal" or "Proximal"</p> <p><strong>Plays</strong> - Number of times the participant felt the stimulus before selecting a response</p> <p> </p> <p><strong>Table Fields - SSW_SecondaryDataset-Zigzag.mat</strong></p> <p><strong>Participant</strong> - Participant label</p> <p><strong>Speed </strong>- Longitudinal speed, *v<sub>l</sub>*, of the focused ultrasound stimulus (in m/s)</p> <p><strong>Response </strong>- Participant response as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>Direction </strong>- True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>isCorrect </strong>- Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition </strong>- Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Distal" or "Proximal"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Distal" or "Proximal"</p> <p><strong>Condition </strong>- Indicates the experimental condition ("NoDelay" or "WithDelay")</p> <p> </p> <p><strong>Table Fields - SSW_SecondaryDataset-Circle.mat</strong></p> <p><strong>Participant </strong>- Participant label</p> <p><strong>Speed </strong>- Linear speed of the focused ultrasound stimulus along the circular trajectory (in m/s)</p> <p><strong>Response </strong>- Participant response as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction)</p> <p><strong>Direction </strong>- True direction of the stimulus as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction)</p> <p><strong>isCorrect </strong>- Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition </strong>- Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Counterclockwise" or "Clockwise"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Counterclockwise" or "Clockwise"</p> <p><strong>Condition </strong>- Indicates the experimental condition ("NoDelay" or "WithDelay")</p>
Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics
<p><strong>Participant demographics:</strong></p> <p>The sample is consisted by 20 healthy volunteers with a mean age of 24.79 years (SD = 3.54 years). The cohort was 68% male and 32% female. In terms of education, 16% had attended only high school, while 32% had a bachelor's degree, 42% a master's degree, and 11% a doctorate. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki.</p> <p><strong>Experiment Description:</strong></p> <p>The experiment consisted in having the subjects perform motor imagery of a bimanual rowing task with two individual paddles, one in each hand, under five experimental conditions. Four of these conditions used NeuRow (<a href="https://link.springer.com/chapter/10.1007/978-3-030-27950-9_1"><strong>Vourvopoulos et al. (2016-2019</strong>))</a>—a VR environment that renders virtual arms from a first-person perspective—while the other conditions used abstract feedback based on the BCI-Graz paradigm<a href="https://ieeexplore.ieee.org/abstract/document/1214714"> (<strong>Pfurtscheller et al. (2003))</strong></a>. All six conditions and their acronyms are described below:</p> <ol> <li><strong>Motor Imagery(MI)</strong>: The standard motor imagery training, with a fixation cross and directional arrows on a black background guiding the subjects through the experiment.</li> <li><strong>Motor Imagery/Motor Observation (MIMO):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor.</li> <li><strong>Motor Imagery/Motor Observation with Haptics (MIMOHP): </strong>A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD (MIMOVR):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD and Haptics (MIMOVRHP):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Execution (ME):</strong> A fixation cross and directional arrows were displayed on a black background through a monitor (same as in MI), and guided the subjects through the experiment by having them tap their fingers accordingly. Data from this condition was available only after S07, so only 10 subjects<br> have performed ME.</li> </ol> <p>Finally, this experiment followed a within-subject design, in a randomized order of the conditions to minimize any order effects, while MI and ME conditions acted as control.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active electrodes(+3 ACC) with a sampling rate of 500Hz. In addition, <strong>ECG, PPG</strong> and <strong>Respiration</strong> signals have been recorded synchronously in a bipolar montage, and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through a monitor in all conditions except in MIMOVR and MIMOVRHP, in which an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA) was used instead. Haptic feedback was provided through the Oculus Rift hand controllers.<br> </p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>PPG</strong> (AUX1): 33<br> <strong>Resp</strong>. (AUX2): 34<br> <strong>ECG</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p> </p> <p><strong>Event codes:</strong></p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>S01</td> <td>Experiment Start</td> </tr> <tr> <td>S02</td> <td>Baseline Start</td> </tr> <tr> <td>S03</td> <td>Baseline Stop</td> </tr> <tr> <td>S04</td> <td>Start Of Trial</td> </tr> <tr> <td>S05</td> <td>Cross On Screen</td> </tr> <tr> <td>S07</td> <td>class1, Left hand </td> </tr> <tr> <td>S08</td> <td>class2, Right hand </td> </tr> <tr> <td>S09</td> <td>Feedback Continuous</td> </tr> <tr> <td>S10</td> <td>End of Trial</td> </tr> <tr> <td>S11</td> <td>End Of Session</td> </tr> <tr> <td>S12</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- USER #<br> | +---SESSION #<br> | | +---TASK #<br> | | | +---MI<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMO<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHP<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHPVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---ME<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk</p> <p> </p> <p><strong>Note: </strong>The first three datasets are from pilot sessions: sub-p01 to p03. From sub-01 to 19, subjects 10 and 11 have been removed due to the lack of markers. Subject sub-13, task MIMOVRHP is missing.</p> <p> </p>
Unequal - but fair? Weights in the serial integration of haptic texture information
<p>The sense of touch is characterized by its sequential nature. In texture perception, enhanced spatio-temporal extension of exploration leads to better discrimination performance due to combination of repetitive information. We have previously shown that the gains from additional exploration are smaller than the Maximum Likelihood Estimation (MLE) model of an ideal observer would assume. Here we test if this suboptimal integration can be explained by unequal weighting of information. Participants stroke 2 to 5 times across a virtual grating and judged the ridge period in a 2IFC task. We presented slightly discrepant period information in one of the strokes in the standard grating. Results show linearly decreasing weights of this information with spatio-temporal distance (number of intervening strokes) to the comparison grating. For each exploration extension (number of strokes) the stroke with the highest number of intervening strokes to the comparison was completely disregarded. The results are consistent with the notion that memory limitations are responsible for the unequal weights. This study raises the question if models of optimal integration should include memory decay as an additional source of variance and thus not expect equal weights.</p> <p><strong>Lezkan</strong>, A. & <strong>Drewing</strong>, K. (2014). Unequal - but fair? Weights in the serial integration of haptic texture information. <em>Haptics: Neuroscience, Devices, Modeling, and Applications</em> (pp. 386-392). Springer: Heidelberg.</p> <p> </p> <p>The Zip file contains all data relative to the publication. The data of each participant is contained in a separate file.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Integration of serial sensory information in haptic perception of softness
<p>Redundant estimates of an environmental property derived simultaneously from different senses or cues are typically integrated according to the Maximum Likelihood Estimation model (MLE): Sensory estimates are weighted according to their reliabilities, maximizing the percept"s reliability. Mechanisms underlying the integration of sequentially derived estimates from one sense are less clear. Here we investigate the integration of seriallysampled redundant information in softness perception. We developed a method to manipulate haptically perceived softness of silicone rubber stimuli during bare finger exploration. We then manipulated softness estimates derived from single movement segments (indentations) in a multi-segmented exploration to assess their contributions to the overall percept. Participants explored two stimuli in sequence, using 2-5 indentations and reported which stimulus felt softer. Estimates of the first stimulus' softness contributed to the judgments similarly, whereas for the second stimulus estimates from later as compared to earlier indentations contributed less. In line with unequal weighting, the percept"s reliability increased with increasing exploration length less than predicted by the MLE model. This pattern of results is well explained by assuming that the representation of the first stimulus fades when the second stimulus is explored, which fits with a neurophysiological model of perceptual decisions (Deco et al., 2010).</p> <p> </p> <p>There are zip files for every experiment (1 & 2a-d), which contain all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) and the staircase reversals for each condition (*.pse files) in separate folders for each session. In every experiment folder there is a list of trials which were excluded from the analyses.</p> <p>Variables of Experiment 1 are described in the file VARIABLE_CODES_EXP1.txt and the variables of Experiment 2a-d are described in the file VARIABLE_CODES_EXP2.txt.</p> <p> </p>
Haptically perceived softness of deformable stimuli can be manipulated by applying external forces during the exploration
<p>The perception of softness is the result of the integration of information provided by multiple cutaneous and kinesthetic signals. The relative contributions of these signals to the combined percept of softness was not yet addressed directly. We transmitted subtle external vertical forces to the exploring human finger during the exploration of deformable silicone rubber stimuli to dissociate the force estimates provided by the kinesthetic signals and the efference copy from cutaneous force estimates. This manipulation introduced a conflict between the cutaneous and the kinesthetic/efference copy information on softness. We measured Points of Subjective Equality (PSE) of manipulated references to stimuli which were explored without external forces. PSEs shifted as a linear function of external force in predicted directions - to higher compliances with pushing and to lower compliances with pulling force. We found relative contribution of kinesthetic/efference copy information to perceived softness being 23% for rather hard and 29% for rather soft stimuli. Our results suggest that an integration of the kinesthetic/efference copy information and cutaneous information with constant weights underlies softness perception. The kinesthetic/efference copy information seems to be slightly more important for the perception of rather soft stimuli.</p> <p>Metzger, A., & Drewing, K. (2015). Haptically perceived softness of deformable stimuli can be manipulated by applying external forces during the exploration. In World Haptics Conference (WHC), 2015 IEEE (pp. 75-81). IEEE.</p> <p> </p> <p>The Zip file contains all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) and the staircase reversals for each condition (*.pse files) in separate folders for each session.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Haptic aftereffect of softness
<p>Past sensory experience can influence present perception. We studied the effect of adaptation in haptic softness perception. Participants compared two silicon rubber stimuli, a reference and a comparison stimulus, by indenting them simultaneously with the index fingers of their two hands and decided which one felt softer. In adaptation conditions the index finger that explored the reference stimulus had previously been adapted to another rubber stimulus. The adaptation stimulus was indented 5 times with a force of >15N, thus the two in-dex fingers had a different sensory past. In baseline conditions there was no previous adaptation. We measured the Points of Subjective Equality (PSEs) of one reference stimulus to a set of comparison stimuli. We used four different adaptation stimuli, one was harder, two were softer and one had approximately the same compliance as compared to the reference stimulus. PSEs shifted as a function of the compliance of the adaptation stimulus: the reference was per-ceived to be softer when the finger had been adapted to a harder stimulus and it was perceived to be harder after adaptation to a softer stimulus. We conclude that recent sensory experience causes a shift of haptically perceived softness away from the softness of the adaptation stimulus. The finding that perceived softness is susceptible to adaptation suggests that there might be neural chan-nels tuned to different softness values and softness is an independent primary perceptual quality.</p> <p>Metzger, A., & Drewing, K. (2016). Haptic Aftereffect of Softness. In F. Bello, H. Kajimoto & Y. Visell (Eds.), Haptics: Perception, Devices, Control, and Applications: 10th International Conference, EuroHaptics 2016, London, UK, July 4-7, 2016, Proceedings, Part I (pp. 23-32). Berlin Heidelberg: Springer.</p> <p> </p> <p>The Zip file contains all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) and the staircase reversals for each condition (*.pse files) in separate folders for each session.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Supplementary Material—Evaluating tactile feedback in addition to kinesthetic feedback for haptic shape rendering: a pilot study
<p>Supplementary Material of the journal article: "<a href="https://www.frontiersin.org/articles/10.3389/frobt.2024.1298537/abstract">Evaluating tactile feedback in addition to kinesthetic feedback for Haptic Shape rendering: a pilot study</a>": DOI: <a href="https://doi.org/10.3389/frobt.2024.1298537">10.3389/frobt.2024.1298537</a></p> <p> </p>
SUN XR haptic interaction dataset
<p>Dataset related to perception experiments for wearable haptic devices worn at the upper limb. It contains hand trajectories, provided fedback signals, and reference signals to follow in a trajectory-tracking task performed in different feedback condition (visual and haptic). Experimental detail of the setup (including kind of tactile feedback and proposed motor task) are in the scientific paper "A Direct-drive, Wearable Armband Device to Experiment Combined Continuous and Vibrotactile Haptic Feedback for Guidance in Motor Tasks" accepted for publication in Eurohaptics 2024 conference</p>
Cooperative aerial tele-manipulation with haptic feedback
<p>Data-set regarding the work "Cooperative aerial tele-manipulation with haptic feedback".</p> <p>Citation bibtex:</p> <p>@inproceedings{mohammadi2016cooperative,<br> title={Cooperative aerial tele-manipulation with haptic feedback},<br> author={Mohammadi, Mostafa and Franchi, Antonio and Barcelli, Davide and Prattichizzo, Domenico},<br> booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},<br> pages={5092--5098},<br> year={2016},<br> organization={IEEE}<br> }<br> </p>
Haptic Saliency Model for Rigid Textured Surfaces
<p>When touching an object, we focus more on some of its parts rather than touching the whole object’s surface, i.e. some parts are more salient than others. Here we investigated how different physical properties of rigid, plastic, relieved textures determine haptic exploratory behavior. We produced haptic stimuli whose textures were locally defined by random distributions of four independent features: amplitude, spatial frequency, orientation and isotropy. Participants explored two stimuli one after the other and in order to promote exploration we asked them to judge their similarity. We used a linear regression model to relate the features and their gradients to the exploratory behavior (spatial distribution of touch duration). The model predicts human behavior significantly better than chance, suggesting that exploratory movements are to some extent driven by the low level features we investigated. Remarkably, the contribution of each predictor changed as a function of the spatial scale in which it was defined, showing that haptic exploration preferences are spatially tuned, i.e. specific features are most salient at different spatial scales.</p> <p>Metzger, A., Toscani, M., Valsecchi, M. & Drewing, K. (2018) Haptic saliency model for rigid textured surfaces. In Prattichizzo, D., Shinoda, H., Tan, H. Z., Ruffaldi, E. & Frisoli, A. (Eds.), Haptics: Science, Technology, and Applications, 11th International Conference, EuroHaptics 2018, Pisa, Italy, June 13-16, 2018, Proceedings, Part I (pp. 389–400). Springer International Publishing, Cham.</p> <p> </p> <p>Data of the experiment is stored in a zip file, containing all data relative to the publication. The 'movement' folder containes participnts' movement data. The 'stimuli' folder containes the 2D and 3D models of the stimuli. </p> <p>Explanaition and coding of the data is provided in the file VARIABLE_CODES.txt.</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.