One of the grand challenges in neuroscience is to understand the developing brain ‘in action and in context’ in complex natural settings. To address this challenge, it is imperative to acquire brain data from freely-behaving children to assay the variability and individuality of neural patterns across gender and age.

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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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Raw data of image files that have been used to analyze the filling yields of the cavities in microscaffolds using murine and human induced stem cell-derived neurons, respectively. 

Instructions: 

The *.lif files can either be opened with the Laica LAS X software or Fiji using the bio-formats plugin.

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Ten volunteers were trained through a series of twelve daily lessons to type in a computer using the Colemak keyboard layout. During the fourth-, eight-, and eleventh-session, electroencephalography (EEG) measurements were acquired for the five trials each subject performed in the corresponding lesson. Electrocardiography (ECG) data at each of those trials were acquired as well. The purpose of this experiment is to aim in the development of different methods to assess the process of learning a new task.

Instructions: 

*Experimental setup

Ten volunteers were trained through a series of twelve daily lessons to type in a computer using the Colemak keyboard layout, which is an alternative to the QWERTY and Dvorak layouts, and it is designed for efficient and ergonomic touch typing in English. Six of our volunteers were female, four male, all of them were right-handed, and their mean age was 29.3 years old with an standard deviation of 5.7 years. The lessons used during our experiment are available on-line at colemak.com/Typing_lessons. In our case, we asked the volunteers to repeat each of them five times (with resting intervals of 2 min in between). We
chose Colemak touch typing as the ability to learn because most people are unaware of its existence, then it is a good candidate for a truly new ability to learn. The training process always took place in a sound-proof cubicle in which the volunteers were isolated from distractions. Hence, the volunteers were sitting in front of the computer and were engaged entirely in the typing lesson. All the experiments were carried at the same hour of the day, and all volunteers were asked to refrain of doing any additional training anywhere else. For more details, see [1].

*Data arrangement

A Matlab-compatible file is provided for each subject. Each .mat file contains a cell array (named Cn) of size 15x10, which corresponds to the 15 trials and 10 channels, respectively. Trials are organized as follows: rows 1-5 correspond to the measurements during the fourth Colemak lesson, rows 6-10 during the eighth, and rows 11-15 during the eleventh. Channels are organized by columns in the following order: (1) ECG, (2) F3, (3) Fz, (4) F4, (5) C3, (6) Cz, (7) C4, (8) P3, (9) POz, and (10) P4. Each of the elements of Cn correspond to a vector containing the output (time samples acquired at 256 Hz sampling frequency) of each of those channels. The lenght of each of those vectors differ between subjects, as well as for each trial depending on the time it took the corresponging subject to complete the Colemak lesson. The units of all output signals are microVolts.

*Preprocessing

All data has been preprocessed with the automatic decontamination algorithms provided by the B-Alert Live Software (BLS): raw signals are processed to eliminate known artifacts. Particularly, the following actions are taken for different type of artifacts:

• Excursions and amplifier saturation – contaminated periods are replaced with zero values, starting and ending at zero crossing before and after each event.
• Spikes caused by artifact are identified and signal value is interpolated.
• Eye Blinks (EOG) – wavelet transforms deconstruct the signal and a regression equation is used to identify the EEG regions contaminated with eye blinks. Representative EEG preceding the eye blink is inserted in the contaminated region.

Aditionally, all data were detrended using Matlab's command detrend.

*How to acknowledge

We encourage researchers to use the published dataset freely and we ask that they cite the respective data sources as well as this paper:

[1] D. Gutiérrez y M. A. Ramírez-Moreno, “Assessing a Learning Process with Functional ANOVA Estimators of EEG Power Spectral Densities,” Cognitive Neurodynamics, vol. 10, no. 2, pp. 175-183, 2016. DOI: 10.1007/s11571-015-9368-7

*Credits

All data were acquired in the Laboratory of Biomedical Signal Processing, Cinvestav Monterrey, in the context of M. A. Ramírez-Moreno's MSc thesis work under the advice of D. Gutiérrez.

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This Dataset contains EEG recordings from epileptic rats. The genetic absence epilepsy rats (GAERS) are one of the best-established rodent models for generalized epilepsy. The rats show seizures with characteristic "spike and wave discharge" EEG patterns. Experiments were performed in accordance with the German law on animal protection and were approved by the Animal Care and Ethics Committee of the University of Kiel.

Instructions: 
  • Sample Frequency: 1600
  • Day1 (18:23:57-16:35:56): Three animals (R1, R2, R3): Array (data points x channels (3))
  • Day2 (16:42:53-16:52:06): Three animals (R1, R2, R3): Array (data points x channels (3))
  • Day3 (17:32:19-10:25:19): Three animals (R1, R2, R3): Array (data points x channels (3))
  • Day4 (10:26:40-14:46:13): Two animals (R1, R3): Array (data points x channels (3))
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Participants were 61 children with ADHD and 60 healthy controls (boys and girls, ages 7-12). The ADHD children were diagnosed by an experienced psychiatrist to DSM-IV criteria, and have taken Ritalin for up to 6 months. None of the children in the control group had a history of psychiatric disorders, epilepsy, or any report of high-risk behaviors.

 

Instructions: 

 

Extract the Zip files. Load the ".mat" data into MATLAB.

 

If you want to import the electrode location into EEGLAB, please use the attached".ced" file.

 

 

 

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The behavioral and ERP Data of online shopping festival experiment

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Dataset asscociated with a paper to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence

"The perils and pitfalls of block design for EEG classification experiments"

The paper has been accepted and is in production.

We will upload the dataset when the paper is published.

This is a placeholder so we can obtain a DOI to include in the paper.

Instructions: 

See the paper "The perils and pitfalls of block design for EEG classification experiments" on IEEE Xplore.

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

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EEG signals of various subjects in text files are uploaded. It can be useful for various EEG signal processing algorithms- filtering, linear prediction, abnormality detection, PCA, ICA etc.

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