Biophysiological Signals

This database contains the 166 Galvanic Skin Response (GSR) signal registers collected from the subjects participating in the first experiment (EXP 1) presented in:

R. Martinez, A. Salazar-Ramirez, A. Arruti, E. Irigoyen, J. I. Martin and J. Muguerza, "A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis," in IEEE Access, vol. 7, pp. 43730-43741, 2019. doi: 10.1109/ACCESS.2019.2908445

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Motor point identification is pivotal to elicit comfortable and sustained muscle contraction through functional electrical stimulation. To this purpose, anatomical charts and manual search techniques are used to extract subject-specific stimulation profile. Such information being heterogenous they lack standardization and reproducibility. To address these limitations; we aim to identify, localize, and characterize the motor points of forearm muscles across nine healthy subjects.

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816 Views

FRAP curve modeling using transient-sensitive analog computer unit with oscilloscopic CRT (Practicum, 2014)

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The data is obtained from electrocardiography, using flexible electrode, Ag/AgCl electrode and Metal Clamp electrode of a femal subject, age 22 years old.

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This Free CAD files are for the manuscript "A temperature-controlled patch-clamp platform demonstrated on Jurkat T lymphocytes and human stem cell derived neurons". The files allow for easily 3D-printing a housing box for the electronics. 

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85 Views

In order to study the application of machine learning in myoelectric data, the machine learning method has been used for data mining and analysis so as to find correlation characteristics. More than 2,300 myoelectric examination data from Sichuan Provincial Hospital of Traditional Chinese Medicine (TCM) for 10 months has been collected and recorded. By means of setting the inclusion criteria and excluding the irrelevant factors, the facial nerve electromyography and auditory brainstem response test reports that meet the research criteria have been screened out.

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This dataset contains post-processed Pattern Reversal Visual Evoked Potential (PR-VEP) signals from 20 subjects, 12 males and 8 females between 24-28 years old. These signals are acquired with two different kinds of instruments: a new prototype based on Smart Glasses and a commercial device for PR-VEP.

For a detailed description of this dataset see accompanying publication "Pattern-Reversal Visual Evoked Potential on Smart Glasses" by Terracciano R., et al. IEEE Journal of Biomedical and Health Informatics 2019, 10.1109/JBHI.2019.2899774.

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The dataset, includes raw data, observations and biometric data from our case study with an individual with DMD, controlling for the first time an active hand orthosis.

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896 Views

The proposed signals are used  for electromagnetic-based stroke classification.  Six realistic head phantom computed from MRI scans, is surrounded by an antenna array of 16 dipole antennas distributed uniformly around the head. These antennas are deployed in a fixed circular array around the head, at a distance of approximately 2-3 mm from the head. A Gaussian pulse covering the bandwidth from 0:7 to 2 GHz is emitted from each of the antennas, sequentially, while all of the antennas capture the scattered signals. Since 16 antennas were used, there are a total of 256 channel signals (i.e.

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3468 Views

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