Brain
This dataset is in support of my following Research papers
Preprint (Make sure you have read Caution) :
- Novel ß Transtibial Prosthetic 9-DoF Artificial Leg Adaptive Controller - Part I*
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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.
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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.
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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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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.
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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"
DOI: 10.1109/TPAMI.2020.2973153
If you use this code or data, please cite the above paper.
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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.
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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.
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