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Related Experiment Video

Updated: Apr 10, 2026

Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes hPSC-CMs Using Multi-electrode Arrays MEAs

Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes hPSC-CMs Using Multi-electrode Arrays MEAs

Published on: May 12, 2017

MultiElec: A MATLAB Based Application for MEA Data Analysis.

Vassilis Georgiadis¹, Anastasis Stephanou¹, Paul A Townsend²

¹Institute of Child Health, University College London, London, United Kingdom.

|June 16, 2015

Summary

This summary is machine-generated.

MultiElec is a user-friendly, open-source MATLAB application for analyzing microelectrode array (MEA) recordings. It simplifies complex data visualization and analysis, including conduction velocity mapping and artifact handling.

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Interfacing Microfluidics with Microelectrode Arrays for Studying Neuronal Communication and Axonal Signal Propagation

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Interfacing Microfluidics with Microelectrode Arrays for Studying Neuronal Communication and Axonal Signal Propagation

Interfacing Microfluidics with Microelectrode Arrays for Studying Neuronal Communication and Axonal Signal Propagation

Published on: December 8, 2018

Area of Science:

Neuroscience
Computational Biology
Bioinformatics

Background:

Microelectrode array (MEA) recordings are crucial for studying neural network activity.
Analyzing MEA data can be complex and time-consuming, requiring specialized software.
Existing tools may lack user-friendliness or comprehensive analysis functionalities.

Purpose of the Study:

To introduce MultiElec, an open-source MATLAB application designed for efficient MEA data analysis.
To provide a user-friendly graphical user interface (GUI) for visualizing and analyzing neural signals.
To offer advanced features for activation time determination, conduction velocity calculation, and artifact handling.

Main Methods:

Development of a MATLAB-based application with an integrated GUI.
Implementation of functions for simultaneous display and analysis of 60-electrode MEA recordings.
Inclusion of tools for activation-time mapping, conduction velocity calculation, and signal artifact suppression.

Main Results:

MultiElec enables simultaneous display and analysis of voltage traces from 60 electrodes.
The application facilitates activation-time determination, heat map generation, and isoline display.
Semi-automatic calculation of local conduction velocities with vector plots and 3D video generation of signal progression are supported.

Conclusions:

MultiElec offers a comprehensive and accessible platform for MEA data analysis.
Its user-friendly GUI and advanced features streamline the analysis of neural network activity.
The open-source nature of MultiElec promotes wider adoption and collaboration in neuroscience research.