Database for storing and organizing data of neurophysiological experiments: description and basic functionality

In present paper, we introduce a database containing multichannel experimental data of brain electrical activity (M/EEG signals) supplemented by necessary experimental protocols and additional characteristics of the subject (ECG, EOG, EMG, etc.), as well as the results of various processing methods application to the initial data. Data management and access is implemented with web-interface containing the toolkit for viewing and editing existing data and adding new items. Developed database provides increased level of data storage reliability and instant access to the neurophysiological data for analysis, processing and evaluation of accumulated experimental information.

[1]  Elena Pitsik,et al.  Approaches for the Improvement of Motor-Related Patterns Classification in EEG Signals , 2019, 2019 3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR).

[2]  Alexander N. Pisarchik,et al.  Numerical simulation of coherent resonance in a model network of Rulkov neurons , 2018, Saratov Fall Meeting.

[3]  Alexander E. Hramov,et al.  Stimulus classification using chimera-like states in a spiking neural network , 2020 .

[4]  A. Hramov,et al.  Formation and nonlinear dynamics of the squeezed state of a helical electron beam with additional deceleration , 2013 .

[5]  Semen A. Kurkin,et al.  Artificial intelligence systems for classifying EEG responses to imaginary and real movements of operators , 2019, Saratov Fall Meeting.

[6]  Semen Kurkin,et al.  Localizing oscillatory sources in a brain by MEG data during cognitive activity , 2020, 2020 4th International Conference on Computational Intelligence and Networks (CINE).

[7]  H. Aurlien,et al.  EEG background activity described by a large computerized database , 2004, Clinical Neurophysiology.

[8]  Vladimir A. Maksimenko,et al.  Coherent resonance in the distributed cortical network during sensory information processing , 2019, Scientific Reports.

[9]  Alexander N. Pisarchik,et al.  Using artificial neural networks for classification of kinesthetic and visual imaginary movements by MEG data , 2020 .

[10]  Alexander E. Hramov,et al.  Artificial Neural Networks as a Tool for Recognition of Movements by Electroencephalograms , 2018, ICINCO.

[11]  A. Hramov,et al.  The development and interaction of instabilities in intense relativistic electron beams , 2015 .

[12]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[13]  Vladimir A. Maksimenko,et al.  Phase-amplitude coupling between mu- and gamma-waves to carry motor commands , 2019, 2019 3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR).

[14]  A. Hramov,et al.  Virtual cathode formation in annular electron beam in an external magnetic field , 2009 .

[15]  Vladimir A. Maksimenko,et al.  Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity , 2018, Complex..

[16]  Semen Kurkin,et al.  Machine learning approaches for classification of imaginary movement type by MEG data for neurorehabilitation , 2019, 2019 3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR).

[17]  E. Gordon,et al.  Integrative Neuroscience: The Role of a Standardized Database , 2005, Clinical EEG and neuroscience.

[18]  A. Hramov,et al.  Chaotic oscillations in electron beam with virtual cathode in external magnetic field , 2011 .

[19]  Shlomo Havlin,et al.  Explosive synchronization coexists with classical synchronization in the Kuramoto model. , 2016, Chaos.

[20]  O. Rosso,et al.  The Australian EEG Database , 2005, Clinical EEG and neuroscience.

[21]  Vladimir A. Maksimenko,et al.  A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications , 2019, ICINCO.

[22]  Maxim O. Zhuravlev,et al.  Recognition of neural brain activity patterns correlated with complex motor activity , 2018, Saratov Fall Meeting.

[23]  Parth Chholak,et al.  Visual and kinesthetic modes affect motor imagery classification in untrained subjects , 2019, Scientific Reports.

[24]  Alexander E. Hramov,et al.  Formation and dynamics of a virtual cathode in a tubular electron beam placed in a magnetic field , 2009 .

[25]  Alexander E. Hramov,et al.  Nonlinear dynamics of the complex multi-scale network , 2018, Saratov Fall Meeting.

[26]  A. Hramov,et al.  Output microwave radiation power of low-voltage vircator with external inhomogeneous magnetic field , 2011 .

[27]  Thomas George Selvaraj,et al.  EEG Database of Seizure Disorders for Experts and Application Developers , 2014, Clinical EEG and neuroscience.