Eview: an open source software for converting and visualizing of multichannel electrophysiological signals

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BACKGROUND: Methods and tools for operating with multichannel electrophysiological signals need to develop and correspond to the speed of data traffic in contemporary experiments. Analyzing and visualizing experimental data with minimal delay and with minimized experimenter effort is a pressing task in the field of neurobiology and requires the use of complex approaches specifically selected for each specific type of experiment. Creating open-source programs that can be promptly adapted for different tasks is one of the approaches that provide the ability to perform complex scientific experiments with high quality.

AIM: This work is aimed at creating open-source software for analytical and visualization support of neurobiological experiments.

METHODS: Software development was performed in MATLAB environment. The program is built on a modular principle and includes an intuitive graphical interface that facilitates control of the signal processing and display.

RESULTS: A software tool was created that allows to optimize and accelerate various stages of electrophysiological research, including preliminary analysis of the quality of the experiment being prepared, in-depth analysis of recorded signals, and preparation of illustrative material for publications.

CONCLUSION: The resulting program has a number of advantages in comparison with similar products in terms of versatility, speed, and availability, and can be used to solve a wide class of research problems.

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作者简介

Andrey Zakharov

Kazan (Volga Region) Federal University; Kazan State Medical University

编辑信件的主要联系方式.
Email: AnVZaharov@kpfu.ru
ORCID iD: 0000-0002-6175-9796
SPIN 代码: 5181-0893

Cand. Sci. (Biol.)

俄罗斯联邦, Kazan; Kazan

Yulia Zakharova

Kazan (Volga Region) Federal University

Email: 3axapova.71@gmail.com
ORCID iD: 0000-0002-4808-3541
SPIN 代码: 6251-5722
俄罗斯联邦, Kazan

参考

  1. Nasretdinov A, Evstifeev A, Vinokurova D, et al. Full-band eeg recordings using hybrid ac/dc-divider filters. eNeuro. 2021;8:4: ENEURO.0246-21.2021. doi: 10.1523/ENEURO.0246-21.2021.
  2. Nasretdinov A, Lotfullina N, Vinokurova D, et al. Direct current coupled recordings of cortical spreading depression using silicone probes. Front Cell Neurosci. 2017;11:408. doi: 10.3389/fncel.2017.00408
  3. Dreier JP, Fabricius M, Ayata C, et al. Recording, analysis, and interpretation of spreading depolarizations in neurointensive care: review and recommendations of the COSBID research group. J Cereb Blood Flow Metab. 2017;37(5):1595–1625. doi: 10.1177/0271678X16654496
  4. Vinokurova D, Zakharov A, Chernova K, et al. Depth-profile of impairments in endothelin-1 — induced focal cortical ischemia. J Cereb Blood Flow Metab. 2022;42:10:1944–1960. doi: 10.1177/0271678X221107422
  5. Lückl J, Lemale CL, Kola V, et al. The negative ultraslow potential, electrophysiological correlate of infarction in the human cortex. Brain. 2018;141(6):1734–1752. doi: 10.1093/brain/awy102

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2. Fig. 1. Main window of ExpressAnalysis module. At the top left there is a list of converted files with source signals, at the bottom there are program messages about the progress of commands. The current signal is shown on the right in the mode of manual correction of the evoked potentials parameters. Red circles correspond to the position of the beginning and end of the rise phase of the evoked response.

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3. Fig. 2. Main window of Eview program. An example of calculating and displaying the color-coded distribution of current source densities along the depth of the cerebral cortex (a, b, channels 1–16) and the color-coded frequency of action potentials (a, channels 17–33) are shown. The black lines show the local field potential (channels 1–33 on fig. a, channel 11–16 on fig. b) and the signals of animal motion sensors (channel 65 on fig. a, channels 17–20 on fig. b).

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4. Fig. 3. Action potentials frequency analysis. An example of displaying the fluctuations of the instantaneous frequency of action potentials in two parts of the cerebral cortex is shown. The black lines show the local field potential, the red vertical bars show individual action potentials, the blue lines show the frequency of action potentials. Graphs for the following combinations of display parameters are shown: a — threshold 4, window 1, precision 1; b — threshold 4, window 20, precision 5; c — threshold 7, window 10, precision 1; d — threshold 7, window 10, precision 10.

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