Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

The DataPort Repository contains the data used primarily for generating Figure 1.

Instructions: 

** Please note that this is under construction, and all data and code is still being uploaded whilst this notice is present. Thank-you. Tom **

All code is hosted as a GIT repository (below), as well as instructions, which can be found by clicking on the link/file called README.md in that repository.

https://github.com/thomasmhall-newcastle/IEEE-TNSRE-2016-lfLFPs

You are free to clone/pull this repository and use it under MIT license, on the understanding that any use of this code will be acknowledged by citing the original paper, DOI: 10.1109/TNSRE.2016.2612001, which is Open Access and can be found here: http://ieeexplore.ieee.org/document/7742994/

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Here we present recordings from a new high-throughput instrument to optogenetically manipulate neural activity in moving

Instructions: 

Datasets used in the publication:

1) List of all the dataset used to generate figure 2 and 3:

  • 20210614_RunHeadandTailRailswithDelays_AML470_0-20-40-60-80intensity

  • 20210615_RunHeadandTailRailswithDelays_AML470_0-20-40-60-80intensity

  • 20210616_RunHeadandTailRailswithDelays_AML470_0-20-40-60-80intensity

  • 20210617_RunHeadandTailRailswithDelays_AML470_0-20-40-60-80intensity

2) List of all the dataset used to generate figure S3:

  • 20210618_RunHeadandTailRailswithDelays_AML67_0-20-40-60-80intensity

3) List of all the dataset folders used to generate figure 4 and table 1:

a) AML67 dataset, open loop: 

  • 20210624_RunFullWormRails_Sandeep_AML67_10ulRet_red,

  • 20210614_RunFullWormRails_Sandeep_AML67_10ulRet_red,

  • 20200902_RunFullWormRails_Sandeep_AML67_10ulret.

b) AML67 dataset, closed loop: 

  • 20210624_RunRailsTriggeredByTurning_Sandeep_AML67_10ulRet_red,

  • 20210614_RunRailsTriggeredByTurning_Sandeep_AML67_10ulRet_red,

  • 20200902_RunRailsTriggeredByTurning_Sandeep_AML67_10ulRet.

4) List of all the dataset folders used to generate figure 4:

a) AML470 dataset, open loop: 

  • 20210723_RunFullWormRails_Sandeep_AKS_483.7.e_mec4_Chrimson_10ulRet_red,

  • 20210721_RunFullWormRails_Sandeep_AKS_483.7.e_mec4_Chrimson_10ulRet_red,

  • 20210720_RunFullWormRails_Sandeep_AKS_483.7.e_mec4_Chrimson_10ulRet_red.

b) AML470 dataset, closed loop: 

  • 20210723_RunRailsTriggeredByTurning_Sandeep_AKS_483.7.e_mec4_Chrimson_10ulRet_red,

  • 20210721_RunRailsTriggeredByTurning_Sandeep_AKS_483.7.e_mec4_Chrimson_10ulRet_red, 

  • 20210720_RunRailsTriggeredByTurning_Sandeep_AKS_483.7.e_mec4_Chrimson_10ulRet_red/AML470_20210720_turns_0uW_3s_new3.mat')

Instructions for accessing files: 

 

1) Here are the details of the naming convention used in the filenames of the datasets used to generate figure 2, 3, S3.

Let us use the folder name “20210614_RunHeadandTailRailswithDelays_AML470_0-20-40-60-80intensity” as an example. The different components of the names are:

a) Date: In the above example, this dataset was collected on 20210614 (YYYYMMDD).

b) Experiment type: "RunHeadandTailRailswithDelays" represents the data collected by stimulating head, tail, or both.

c) Name of the strain: The above example shows the dataset collected from "AML470" strain. 

d) Experiment specific information: The tag “0-20-40-60-80intensity” says that 0, 20, 40, 60, and 80uW intensity was used during this dataset.

Moreover, once you go inside each folder, you will see many subfolders with the date and time stamp. For e.g. a subfolder in the above folder is Data20210614_141921_BoxA-PC which basically says the time stamp at which this dataset was recorded (DataYYYYMMDD_HHMMSS) followed by the name of the experimental box (BoxA-PC or BoxB-PC or BoxC-PC or BoxD-PC).

2) Here are the details of the naming convention used in the filenames of the datasets used to generate figure 4 and table 1.

Let us use the folder name “20210624_RunRailsTriggeredByTurning_Sandeep_AML67_10ulRet_red” as an example. The different components of the names are:

a) Date: In the above example, this dataset was collected on 20210624 (YYYYMMDD).

b) Experiment type: "RunRailsTriggeredByTurning" represents the data collected in closed loop protocol whereas "RunFullWormRails" represents the data collected in open loop protocol. Thus, the above example represents a dataset collected using closed loop protocol.

c) Name of the user: In the above example, the user is "Sandeep".

d) Name of the strain: The above example shows the dataset collected from "AML67" strain. Folders containing the datasets collected from AML470 will say "AKS_483.7.e_mec4_Chrimson".

e) ATR information: All the datsets used retinal on the OP50 plates to grow the worms, and hence have the tag "10ulRet".

f) Color of the stim: "red" tag says that in this protocol red stimulus was delivered to the worms.

Moreover, once you go inside each folder, you will see many subfolders with the date and time stamp. For e.g. a subfolder in the above folder is Data20210624_105852 which basically says the time stamp at which this dataset was recorded (DataYYYYMMDD_HHMMSS).

Details of files inside each experimental folder:

The dataset is in the form of the output of the real-time LabVIEW instrument for maximum compression. It still needs to go through post-processing before further analysis.

 

Post-processing can be done by running the /ProcessDateDirectory.m MATLAB script from the code repository.

 

It is organized into date directories, which aggregate all the experiments collected on the same day. 

 

Each experiment is it's own time stamped folder within a date directory, and it contains the following files:

- camera_distortion.png contains camera spatial calibration information in the image metadata

- CameraFrames.mkv is the raw camera images compressed with H.265

- labview_parameters.csv is the settings used by the instrument in the real-time study

- labview_tracks.mat contains the real-time tracking data in a MATLAB readable HDF5 format

- projector_to_camera_distortion.png contains the spatial calibration information that maps projector pixel space into camera pixel space

- tags.txt contains tagged information for the experiment and is used to organize and select experiments for analysis

- timestamps.mat contains timing information saved during the real-time experiments, including closed-loop lag.

- ConvertedProjectorFrames folder contains png compressed stimulus images converted to the camera's frame of reference.

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The University of Turin (UniTO) released the open-access dataset Stoke collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP).

Instructions: 

Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset.

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Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. These data are currently housed in Georgetown University's G-DOC System and are described in a related manuscript .

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This dataset consists of EEG data of 40 epileptic seizure patients (both male and female) of age from 4 to 80 years. The raw data was collected from Allengers VIRGO EEG machine at Medisys Hospitals, Hyderabad, India. The EEG electrodes were placed according to 10 – 20 International standard. The EEG data was recorded from 16 channels (FP2-F4, F4-C4, C4-P4, P4-O2, FP1-F3, F3-C3, C3-P3, P3-O1, FP2-F8, F8-T4, T4-T6, T6-O2, FP1-F7, F7-T3, T3-T5, and T5-O1) at 256 samples per second.

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Recent advances in computational power availibility and cloud computing has prompted extensive research in epileptic seizure detection and prediction. EEG (electroencephalogram) datasets from ‘Dept. of Epileptology, Univ. of Bonn’ and ‘CHB-MIT Scalp EEG Database’ are publically available datasets which are the most sought after amongst researchers. Bonn dataset is very small compared to CHB-MIT. But still researchers prefer Bonn as it is in simple '.txt' format. The dataset being published here is a preprocessed form of CHB-MIT. The dataset is available in '.csv' format.

Instructions: 

Procedure :

  1. The tool used for preprocessing is Anaconda-Jupyter Notebook on Intel 8th gen i5 processor with 8GB RAM
  2. The dataset is prepared by extracting datapoints from '.edf' by using mne package in python. Equal amount of preictal and ictal data are extracted.
  3. A period of 4096 seconds (68 minutes) each of preictal and ictal data is extracted from the '.edf' files. All ictal periods for 24 patients annotated have been included in the dataset.
  4. Datapoints are loaded and preprocessed as dataframes by using pandas package in python.
  5. System RAM size should be available to the maximum possible extent as dataframes are large.
  6. The file chbmit_preprocessed_data.csv can be used as is for machine learning and deep learning models.

Data Availability :

The datset contains following files.

  • chbmit_ictal_raw_data.csv : This file contains only ictal data from all 24 patients. The channels vary largely and amount to 96 columns in this file.
  • chbmit_preictal_raw_data.csv : This file contains only preictal data from all 24 patients. The channels vary largely and amount to 96 columns in this file.
  • chbmit_preictal_23channels_data.csv :This file contains only preictal data from all 24 patients. Only 23 channels are retained and amount to 23 columns in this file.
  • chbmit_ictal_23channels_data.csv :This file contains only ictal data from all 24 patients. Only 23 channels are retained and amount to 23 columns in this file.
  • chbmit_preprocessed_data.csv :This file contains balanced preictal and ictal data from all 24 patients. Only 23 channels are retained, outcome column is added and amount to 24 columns in this file. In outcome column '0' indicates preictal and '1' indicates ictal.

This dataset is prepared with data reduction techniques. Data cleaning and data transformation need to be done as suitable for the application or model under development. 

Original Data:

 

The original raw dataset in '.edf' is available at https://physionet.org/content/chbmit/1.0.0/  and to be cited as 

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220

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This data set contains:

 

-88 patients

 

-the noncontrast computed tomography (NCCT) and computed tomography angiography (CTA) performed before thrombectomy.

 

-the VOI of blood clot for NCCT and CTA.

 

For each patient NCCT data is marked "2" and CTA is marked "1".

Instructions: 

For each patient NCCT data is marked "2" and CTA is marked "1".

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Dataset asscociated with a paper in Computer Vision and Pattern Recognition (CVPR)

 

"Object classification from randomized EEG trials"

 

If you use this code or data, please cite the above paper.

Instructions: 

See the paper "Object classification from randomized EEG trials" on IEEE Xplore.

 

Code for analyzing the dataset is included in the online supplementary materials for the paper.

 

The code from the online supplementary materials is also included here.

 

If you use this code or data, please cite the above paper.

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The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.

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