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13 results for “VEP”
VEP_workflow_simulatedData
<p>This dataset is used for the paper: </p> <p>Wang, H. E., Woodman, M., Triebkorn, P., Lemarechal, J.-D., Jha, J., Dollomaja, B., Vattikonda, A. N., Sip, V., Medina Villalon, S., Hashemi, M., Guye, M., Makhalova, J., Bartolomei, F., & Jirsa, V. (2023). Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy. <em>Science Translational Medicine</em>, <em>15</em>(680). <a href="https://doi.org/10.1126/scitranslmed.abp8982">https://doi.org/10.1126/scitranslmed.abp8982</a></p> <p>Code to read and process this dataset: <a href="http://zenodo.org/record/7573382#.Y9PN9C8w3RI">https://zenodo.org/record/7573382#.Y9PN9C8w3RI</a></p> <p>It includes both anatomical and simulated data for one example patient. The anatomical data includes the personalized brain surface meshes, gain matrix from SEEG electrodes and source brain regions, and global structural connectivity matrices. The functional data includes the neural field simulation on both vertices and SEEG sensors. The datasets are used in BIDS format. The codes to read and use this dataset are available in https://github.com/HuifangWang/VEP_INS_workflow. </p> <p> </p>
Visual-Evoked Potential (VEP) Event-Related Files from the General Anesthesia and Brain Activity (GABA) Study and Infant Sibling Project (ISP)
<p>HAPPE+ER software was optimized for developmental data using a subset of EEG files from 4-month and 10-month old infants in the General Anesthesia and Brain Activity (GABA) Study. While medically necessary, 1-2 million infants each year undergo general anesthesia – a process that sedates brain activity and impacts early sensory experiences during a time typically characterized by rapid neurocognitive development. The GABA study examines sensory and socioemotional neurodevelopment longitudinally from infancy through childhood in individuals who have and who have never undergone general anesthesia during different windows in the first year of life. The GABA study was carried out in accordance with the recommendations of the Institutional Review Board at Boston Children’s Hospital. All caregivers provided assent for their child’s participation in the GABA study and for the release of the deidentified data. </p> <p>To facilitate the use and understanding of HAPPE+ER software, we have provided a subset of the validation files from the GABA study to serve as a tutorial dataset for how to run event-related potential (ERP) data through this automated processing pipeline. Five files (a.raw - e.raw) are from four 4-month and one 10-month old infants during a pattern reversal visual-evoked potential (VEP) paradigm. Pattern reversal occurred every 500 milliseconds. The pattern stimulus onset is indicated in each file by the code: vep+. Data was collected using a 128-channel EGI HydroCel Geodesic Sensor Net and EGI Net Amps 400, sampled at 1000Hz with an online reference to channel CZ. </p> <p>We have also included a subset of files from the Infant Sibling Project (ISP), an investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life. Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes. The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study. All files here have been deidentified, including alteration of exact acquisition dates. Acquisition times have not been altered. For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:</p> <ol> <li>Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.</li> <li>Gabard-Durnam, L.J., Wilkinson, C., Kapur, K. et al. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat Commun 10, 4188 (2019). <a href="https://doi.org/10.1038/s41467-019-12202-9">https://doi.org/10.1038/s41467-019-12202-9</a></li> </ol> <p>Here we provide a subset of the full dataset with a simulated VEP signal added into the data, as example files for HAPPE+ER. To create these files, we selected a subset of 39 spatially-distributed channels in the baseline EEG files and created sixteen 30-second files using continuous segments of relatively artifact-free (clean) baseline data from the full-length files. Next, from 30-second sections of the same individuals’ EEG that were artifact-laden, we ran ICA and extracted artifact independent components (identified by an expert and labeled artifact by both ICLabel and MARA automated algorithms). We inserted the artifact ICs into that individual’s clean 30-second data segment to create an additional 16 artifact-added files. We then selected a channel from a simulated VEP dataset (included here as simulated_full.set) with a stereotyped and prominent simulated VEP waveform, in this case Oz, and added its timeseries (included here as simulated_singleChan.set) to each channel of the clean and artifact-added files to create two VEP datasets with a known ERP morphology (sim-artifact_a-p and sim-clean_a-p). For additional information about the creation of this simulated data and VEP data with a known ERP morphology, please refer to Monachino et al., in revision; DOI: https://doi.org/10.1101/2021.07.02.450946.</p> <p>Additional files included below are the HAPPE+ER data and pipeline quality metric output spreadsheets for the five GABA study data files for an example run, the output spreadsheet containing the ERP timeseries from the generateERPs script, the .mat file containing the parameter settings for HAPPE+ER for that run, an Excel file with the bad channels for each file, and a tutorial document illustrating the results of this example run. </p>
5-Class Burst C-VEP with Dry EEG
<p><strong>Participants</strong></p> <p>The experiment was conducted with twenty four healthy volunteers (4 women, mean age = 29.3 years, SD = 7.5), all students and staff at ISAE-SUPAERO. None of the participants reported any of the exclusion criteria (neurological antecedents, being under psychoactive medication at the time of the study) and had normal or corrected-to-normal vision. The study was approved by the ethics committee of the University of Toulouse (CER approval number 2023-749) and was carried in accordance with the declaration of Helsinki. Participants gave informed written consent prior to the experiment</p> <p><strong>Experimental Protocol</strong></p> <p>Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded using the dry 8-electrodes Enobio system at a sample rate of 500Hz to record the surface brain activity. The 8 electrodes were positioned over the occipital and parieto-occipital sites: PO7, O1, O2, O3, PO8, PO3, POz, PO4. EEG data and markers were synchronized during recording using Lab Streaming Layer. Once equipped with the dry 8-electrode EEG system, volunteers were asked to focus on five targets that were cued sequentially in a random order for 0.5s, followed by a 2.2s stimulation phase, before a 0.7s inter-trial period. The cue sequence for each trial was pseudo-random and different for each block. After each block, a pause was observed and subjects had to press the space bar to continue. The participants were presented with fifteen blocks of five trials for each of the three conditions (Gabor-based textures, Ricker-based textures or Plain stimuli). The stimuli were presented on the following LCD 24.5'' monitor: Iiyama Gold Phoenix G-Master GB2590HSU-B1, 1920x1080 pixels, 400 cd/m², and the refresh rate has been set to 60Hz.</p> <p><strong>Preprocessing and Lag Correction<br></strong></p> <p>No pre-processing was applied on these data, and they have been neither sliced or epoched, although it remains possible to do so with the help of the markers. However, note that the Enobio streaming system we used introduced a constant 80ms lag in its data. The data provided in the dataset has not been corrected as they we only present raw data without modifications. We advise users of this dataset to correct this by shifting the EEG data 80ms for precise timing.</p> <p><strong>Electrode Location and Naming</strong></p> <p>In the dataset files, electrodes are named EEG001, ..., EEG007, the generic naming of the electrodes with the Enobio system we used. We provide along the dataset files a .loc file to map these generic names to standard 10-20 naming scheme and their respective location.</p>
c-VEP-based BCI for PIN pad task on a T9 layout
<p># Participants<br> Eleven 11 volunteers (2 woman, mean age 28) with normal or corrected-to-normal vision participated in this experiment in our laboratory. The participants did not report any of the exclusion criteria (epilepsy, neurological antecedents, being under psychoactive medication). The study was approved by the ethics committee of the University of Toulouse (CER approval number 2020-334) and carried in accordance with the declaration of Helsinki. Participants gave informed written consent, prior to the experiment. The consent form included a section to approve the online data sharing.</p> <p># Experimental protocol<br> We used the BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a refresh rate of 500Hz to record the surface brain activity. The 32 electrodes followed the 10-20 international system. The ground electrode was placed at the Fpz electrode location and all electrodes were referenced to FCz electrode. The electrodes impedance were brought below 20 $k\Omega$ prior to the recording. We used a single maximum length sequence with 11 different shifts to animate the stimulation for the different targets. The presentation was rendered on an LCD monitor: LG GN 750, 1027x768 pixels, 400 cd/m^2, and 60Hz refresh rate. EEG data and markers were synchronized during recording using Lab Streaming Layer.<br> <br> Subjects were asked to follow cues on a T9 layout to emulate a PIN code typing task (11 classes problem). It was implemented in Python, using the Psychopy toolbox. The 11 stimuli were circles with radius of 150 pixels, with a margin of 250 pixels vertically and horizontally and the figure in the middle.<br> Fifteen 11-trial blocks were recorded for each subject. During a trial, one of the 11 targets was cued for 0.5s, followed by 2.2s of stimulation with a 0.7s inter-trial before next one. The cue sequence for each trial was pseudo-random and different for each block. After each block a pause was observed and subjects had to press space bar to continue.<br> <br> # Pre-processing<br> No pre-processing was applied and data are neither sliced or epoched but it is possible to do it with the help of the markers.</p>
4-class code-VEP EEG data
<p><strong>Participants</strong></p> <p>Twelve healthy volunteers (4 women, mean age: 30.6 years, standard deviation: 7.1), all students and staff at ISAE-SUPAERO with normal or corrected-to-normal vision took part in this study. None of the participants reported any of the exclusion criteria (neurological antecedents and being under psychoactive medication at the time of the study). The study was approved by the ethics committee of the University of Toulouse (CER approval number 2020-334) and was carried in accordance with the declaration of Helsinki. Participants gave informed written consent prior to the experiment.</p> <p><strong>Experimental protocol</strong></p> <p>EEG data were recorded using a BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a refresh rate of 500Hz to record the surface brain activity. The 32 electrodes followed the 10-20 international system. The ground electrode was placed at the FPz electrode location and all electrodes were referenced to the FCz electrode. The electrode impedances were brought below 25kOhm before the recording. EEG data and markers were synchronized during recording using Lab Streaming Layer.</p> <p>Participants were comfortably sat and requested to read and sign the informed consent. Once equipped with the EEG system, volunteers were asked to focus on four targets that were cued sequentially in a random order for 0.5s, followed by a 2.2s stimulation phase, before a 0.7s inter-trial period. The cue sequence for each trial was pseudo-random and different for each block. After each block, a pause was observed and subjects had to press the space bar to continue. In total, each volunteer was presented fifteen four-trial blocks for each of the four conditions, 2 types of c-VEP codes x 2 different amplitudes depth (40% or 100%). The four flickers were all 150 pixels wide discs, without borders, and were presented on the following LCD monitor: Dell P2419HC, 1920x1080 pixels, 265cd/m<sup>2</sup>, and 60Hz refresh rate.</p> <p><strong>Preprocessing</strong></p> <p>No pre-processing was applied on these data, and they have been neither sliced or epoched, although it remains possible to do so with the help of the markers.</p>
How Accurately Does the Diopsys Visual Evoked Potential (VEP) Vision Testing System Detect Glaucoma?
ClinicalTrials.gov study NCT02622178. IPD Sharing: NO. Countries: 1. Publications: 1.
Marginal imprint of human land use upon fire history in a mire-dominated boreal landscape of the Veps Highland, North-West Russia
<p>Spatially explicit reconstructions of fire activity in European boreal forests are rare, limiting our understanding of factors driving vegetation dynamics in this part of the boreal domain. We have developed a spatially explicit dendrochronological reconstruction of a fire regime in a mire-dominated landscape of the Veps Nature Park (North-West Russia) over the 1580-2000 CE period.</p> <p>We dated 74 fire years using 164 fire-scarred living and dead Scots pine (Pinus sylvestris L.) trees collected on 31 sites. The historical fire cycle was 91.4 years (90% confidence intervals, CI 66.2–137.6 years) over the 1580–1720 period, decreasing to 35.9 (CI 28.1–47.6 years) between 1730 and 1770, and then increasing again to 122.7 years (CI 91.0–178.0 years) over the 1780–2000 period. The reconstructed forest fire history featured a number of patterns clearly deviating from the trends documented in previous Northern European reconstructions. The most striking feature was the absence of a period with increased fire activity during the 1600s, a pattern widely observed in Fennoscandia and in Russian Karelia. We noted, however, a higher fire activity period between 1730 and 1780, resulting from the increase in early season fires.</p> <p>Land-use history of the area did not appear to have an effect on historical fire dynamics. The current FC in the Veps Highland is close to the estimates reported for the pre-industrial colonisation period in Fennoscandia, which suggests that the area's forests currently maintain their close-to-natural fire regime.</p>
Marginal imprint of human land use upon fire history in a mire-dominated boreal landscape of the Veps Highland, North-West Russia
Open the record for dataset details and reuse information.
Safety and Efficacy of Using SightSaver Visual Evoked Potential (VEP) for VEP Monitoring in Prone Spine Surgery
ClinicalTrials.gov study NCT02643615. IPD Sharing: NO. Countries: 1. Publications: 0.
Vocational Empowerment Photovoice (VEP)
ClinicalTrials.gov study NCT02784938. IPD Sharing: NO. Countries: 1. Publications: 0.
Improving EPilepsy Surgery Management and progNOsis Using Virtual Epileptic Patient Software (VEP)
ClinicalTrials.gov study NCT03643016. IPD Sharing: NO. Countries: 1. Publications: 0.
Characterisation of Cortical Vestibular Evoked Potentials (C-VEPs)
ClinicalTrials.gov study NCT02463695. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ELDIAdata: Interview data – Veps in Russia
<p>This digital archive contains interview data with the Veps minority speakers in Russia. The archive consists of audio files (available in .wav format) containing individual and focus group interviews with the speakers of four different age groups. All interview files were named using a special coding system. Each file name includes: a) a country where the research was conducted; b) the speech community studied; c) the form of an interview; d) the type of the target group; e) the age group and gender; f) the date of the interview (DDMMYEAR). For the full list of files, as well as code values please refer to the descriptions under “ELDIAdata: Metadata”.</p>
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