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.
This dataset is in support of my following Research papers
Preprint (Make sure you have read Caution) :
https://doi.org/10.36227/techrxiv.19758517.v1
https://doi.org/10.36227/techrxiv.19762762.v1
This is two-part open-access webpage of 'Data:B-Bio Models'.This webpage contains datasets of 'Computational Biology'and Novel ß-Bio models, project folders, with clinical and pharmacy investigation in simulation, proposed by me which are Self-Claimed advancements, in support of my Research claims, discoveries, presentations and books. All content can be freely downloaded after sign-up.
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.
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.
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.
This dataset has been collected in the Patient Recovery Center (a 24-hour, 7-day nurse staffed facility) with medical consultant from the Mobile Healthcare Service of Hamad Medical Corporation.
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"
DOI: 10.1109/TPAMI.2020.2973153
If you use this code or data, please cite the above paper.
