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.

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.

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.

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.

This code was used in some previous our articles, such as:

NGM software for applied neurogoniometry. See our previous articles.

See .doc-fie in the attachement.

Accelerated photoinduced kinetics of electric impedance shifting under UV irradiation.

Oscillations of plant leaf resistance / impedance (10x time-lapse registration).

Kinetic registration of plant leaf resistance (10x).