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An auditory ERP (Event-Related Potentials) paradigm based on auditory stream segregation for 3-class BCI application was investigated by EEG measurements. This dataset contains 64ch EEG (electroencephalography) and 2ch EOG (electrooculography) recorded data.

Abstract Background Stroke management require rapid identification, assessment, and transport of patients to qualified health care centers. However, there is little description in the literature on the multiple challenges associated with the pre-hospital transport of suspected stroke patients. Objective To characterize the pre-hospital care provided to suspected stroke patients by the Brazilian Emergency Medical Service (SAMU in Portuguese), by means of a descriptive case study. Methods This is a descriptive study of a series of cases. Data from the SAMU regarding the responses to emergency calls from suspected stroke patients were collected. Independent reviewers confirmed the diagnostic hypothesis and all discordances were assessed using kappa statistics. Clinical data and transport times were described as frequency and proportion or central tendency and dispersion measures. Normality of continuous variable distribution was assessed using the Kolmogorov-Smirnov test. The Mann-Whitney U test was used for comparison of medians, with a 5% significance level. Results During the studied period, 556 suspected stroke patients were treated. The kappa index was 0.82 (95% CI 0.737 to 0.919) CI. In 74.7% of the cases, the symptom onset time was not recorded. The median time elapsed between the call for emergency services and the ambulance arrival was 18 minutes, and the median transport time was 38 minutes. A total of 34% of the patients were taken to referral hospitals for stroke. Conclusion This study revealed a low level of knowledge regarding the need to determine the exact time of symptom onset of suspected stroke patients. Also, the study showed the low rate of patients taken to referral hospitals. (Int J Cardiovasc Sci. 2021; [online].ahead print, PP.0-0)

Additional file 1:. Supplementary data.

This dataset contains cytoarchitectonic maps of Area ifj1 (IFS/PreCS) in the BigBrain. The mappings were created using cytoarchitectonic criteria applied on digitized histological sections of 1 ��m resolution, cut in coronal plane. Areal borders have been detected by an oberserver-independent border definition (Schleicher 2000). Mappings are available on sections of the BigBrain and have been transformed to the 3D reconstructed BigBrain space using the transformations used in Amunts et al. 2013. From these delineations, a preliminary 3D map of Area ifj1 (IFS/PreCS) has been created by simple interpolation of the coronal contours in the 3D anatomical space of the Big Brain. This map gives a first impression of the location of this area in the Big Brain, and can be viewed in the atlas viewer using the URL below.

This archive contains sample output files for the sample data accompanying the Princeton Handbook for Reproducible Neuroimaging. Outputs include the NIfTI images converted using HeuDiConv (v0.8.0) and organized according to the BIDS standard, quality control evaluation using MRIQC (v0.15.1), data preprocessed using fMRIPrep (v20.2.0), and other auxiliary files. All outputs were created according to the procedures outlined in the handbook, and are intended to serve as a didactic reference for use with the handbook. The sample data from which the outputs are derived were acquired (with informed consent) using the ReproIn naming convention on a Siemens Skyra 3T MRI scanner. The sample data include a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by Yeshurun et al., 2017 and is available as part of the Narratives collection, and the emotional faces task is similar to Chai et al., 2015. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.

EFA with raters left in sample 1

This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available here and here. The package contains seven Comma-Separated Values (CSV) files and two PDF files: master_stage1_anonymized.csv: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded); master_stage2_snr1_anonymized.csv: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded); master_stage2_snr2_anonymized.csv: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded); submission_form_stage1.pdf: PDF export of the online form used in stage 1; submission_form_stage2.pdf: PDF export of the online form used in stage 2. For the manuscript, we analyzed these CSV files with scripts reported here. Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names: normalized rmse: Whole-brain root-mean-squared error relative to ground truth; rmse_detrend_tissue: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask; rmse_detrend_blood: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask; rmse_detrend_DGM: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate); DeviationFromLinearSlope: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0; CalcStreak: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth; DeviationFromCalcMoment: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model); Submission Identifier: Self-chosen unique identifier of the submission; Submission Identifier of the corresponding Stage 1 submission: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1; Changes with respect to Stage 1 submission: Self-reported information about modifications made to the algorithm for Stage 2; Number of submissions in Stage 2: The number of solutions that were submitted to Stage 2 with a similar algorithm; Sim1/Sim2: Filename of the submitted solutions for Stage 1; File name of the zip-file you are going to upload: Filename of the file uploaded to Stage 2; Full name of the algorithm: Self-reported full name of the algorithm used; Preferred Acronym: Self-reported acronym of the algorithm used; Algorithm-type: Self-reported type of algorithm used; Does your algorithm incorporate information derived from magnitude images?: Self-reported Yes/No; Regularization terms: Self-reported types of regularization terms involved; Did your algorithm use the provided frequency map or the four individual echo phase images?: Self-reported information about involved magnitude information; Publication-ready description of the reconstruction technique: Self-reported description of the algorithm; Publications that describe the algorithm: Self-reported literature reference; Algorithm publicly available?: Self-reported public availability of the algorithm; If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?: Self-reported willingness to share the algorithm code with the public; Specific information about this solution: Self-reported detailed information about the solution; Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge: Self reported agreement with publication of submitted solution; Ground truth was not explicitly or implicitly incorporated into your algorithm or solution: Self-reported confirmation that the ground truth was not incorporated in the solution.

The Metadata files contain metadata and behavioural data. The variables are: • acquisition_rate is a scalar describing the acquisition rate in Hz. • Pixel_size is a scalar describing the size of each pixel in microns. • Numb_patches is a scalar describing the number of patches in the experiment. • Patch_coordinates is a structure containing coordinate information about each patch. Patch_coordinates.data is a matrix in which each row represents a patch, and columns 5, 6, and 7 represent the X, Y, and Z positions (respectively) of that patch. • SpeedDataMatrix and SpeedTimeMatrix are vectors containing the wheel speed time series and times from the wheel encoder. • dlc_whisk_angle and dlc_whisk_time are vectors containing the whisking angle time series and times as determined via DeepLabCut.• wheel_MI is a matrix whose second column contains the wheel motion index time series as determined from the wheel cameras and whose second column contains the corresponding times. Note that this file may also contain variables extracted by now obsolete methods which were not included by the analysis in the paper (e.g., Whiskers_angle_0 for old whisker position detection, Axon_dFF for old grouping procedure). You can ignore these.

This dataset contains the distinct architectonic Area 7P (SPL) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area 7P (SPL). The probability map of Area 7P (SPL) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area 7P (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/AHQS-ZR8](https://doi.org/10.25493%2FAHQS-ZR8) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/C3HS-8R7](https://doi.org/10.25493%2FC3HS-8R7) The most probable delineation of Area 7P (SPL) derived from the calculation of a maximum probability map of all currently released JuBrain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)