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.
We develop a potential biomarker to subdivide the stress groups into eustress and distress groups using hemodynamic responses of functional near-infrared spectroscopy (fNIRS). We stimulate two stress groups divided by saliva alpha-amylase (sAA) with an international affective picture system (IAPS) inducing positive or negative emotions and measure hemodynamic responses at the same time. As a result, we have developed a newly designed biomarker using fNIRS.
EEG brain recordings of ADHD and non-ADHD individuals during gameplay of a brain controlled game, recorded with an EMOTIV EEG headset. It can be used to design and test methods to detect individuals with ADHD.
For details, please see:
Alaa Eddin Alchalabi, S. Shirmohammadi, A. N. Eddin and M. Elsharnouby, “FOCUS: Detecting ADHD Patients by an EEG-Based Serious Game”, IEEE Transactions on Instrumentation and Measurement, Vol. 67, No. 7, July 2018, pp. 1512-1520.
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.
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 .
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.
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:
 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
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.
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.
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.
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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Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence
"The perils and pitfalls of block design for EEG classification experiments"
If you use this code or data, please cite the above paper.
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.
The code and the appendix from the online supplementary materials are also included here.
If you use this code or data, please cite the above paper.
BCI-Double-ErrP-Dataset is an EEG dataset recorded while participants used a P300-based BCI speller. This speller uses a P300 post-detection based on Error-related potentials (ErrPs) to detect and correct errors (i.e. when the detected symbol does not match the user’s intention). After the P300 detection, an automatic correction is made when an ErrP is detected (this is called a “Primary ErrP”). The correction proposed by the system is also evaluated, eventually eliciting a “Secondary ErrP” if the correction is wrong.
A detailed description of the data is given in “BCI-Double-ErrP-Dataset-instructions.pdf” and a Matlab code example is provided to extract P300 and ErrPs (primary and secondary).
There are 4 folders, one with the datasets of the P300 calibration (session 1), one with the datasets of the ErrP calibration (session 1), one with the datasets of the testing session (session 2), and a folder with the Matlab code to run the example.
Ear-EEG recording collects brain signals from electrodes placed in the ear canal. Compared with existing scalp-EEG, ear-EEG is more wearable and user-comfortable compared with existing scalp-EEG.
** Please note that this is under construction, and instruction is still being updated **
6 adults ( 2 males/ 4 females, age:22-28) participated in this experiment. The subjects were first given information about the study and then signed an informed consent form. The study was approved by the ethics committee at the City University of Hong Kong(Reference number: 2-25-201602_01).
Hardware and Software
We recorded the scalp-EEG using the a Neuroscan Quick Cap (Model C190) . Ear-EEG were recorded simultaneously with scalp-EEG. The 8 ear electrodes placed at the front and back ear canal (labeled as xF, xB), and two upper and bottom positions in the concha (labeled as xOU and xOD). All ear and scalp electrodes were referenced to a scalp REF electrode. The scalp GRD electrode was used as a ground reference. The signals were sampled at 1000 Hz then filtered with a bandpass filter between 0.5 Hz and 100 Hz together with a notch filter to suppress the line noises. The recording amplifier was SynAmps2, and Curry 7 was used for real-time data monitoring and collecting.
Subjects were seated in front of a computer monitor. A fixation cross presented in the center of the monitor for 3s, followed by an arrow pseudo-randomly pointing to the right or left for 4s. During the 4 s arrow presentation, subjects needed to imagine and grasp the left or right hand according to the arrow direction. A short warning beep was played 2 s after the cross onset to call the subjects.
The data and the metadata from 6 subjects are stored in the IEEE Dataport. Note that Subject 1-4 completed 10 blocks of trials, subject 6 finished only 5 blocks. Each block contained 16 trials. In our dataset, each folder contain individual dataset from one subject. For each individual dataset, there were four type of files (.dat, .rs3, .ceo, .dap). All four files were needed for EEGLAB and MNE package processing. Each individual dataset contains the raw EEG data from 122 channels (from scale EEG recording), 8 channels (from ear EEG recording), and 1 channels (REF electrode).
Individual dataset of subject 1,5,6 has different sub-datasets. The index indicates the time order of that sub-dataset (motor1, then followed by motor2, motor3, motor 4 etc). While Individual dataset of subject 2,3,4 has one main dataset.
Each dataset has timestamps for epoch extraction. Two event labels marked the start of the arrow, which indicated the start of subject hand grasping (event number 1: left hand; event number 2: right hand).