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J Neurosci Methods. 2015 Mar 15;242:118-26. doi: 10.1016/j.jneumeth.2015.01.017. Epub 2015 Jan 19.

AnyWave: a cross-platform and modular software for visualizing and processing electrophysiological signals.

Author information

1
INSERM, UMR1106, Marseille, France; Aix-Marseille Université, Institut de Neurosciences des Systèmes, Marseille, France. Electronic address: Bruno.Colombet@univ-amu.fr.
2
INSERM, UMR1106, Marseille, France; Aix-Marseille Université, Institut de Neurosciences des Systèmes, Marseille, France.

Abstract

BACKGROUND:

The importance of digital signal processing in clinical neurophysiology is growing steadily, involving clinical researchers and methodologists. There is a need for crossing the gap between these communities by providing efficient delivery of newly designed algorithms to end users. We have developed such a tool which both visualizes and processes data and, additionally, acts as a software development platform.

NEW METHOD:

AnyWave was designed to run on all common operating systems. It provides access to a variety of data formats and it employs high fidelity visualization techniques. It also allows using external tools as plug-ins, which can be developed in languages including C++, MATLAB and Python.

RESULTS:

In the current version, plug-ins allow computation of connectivity graphs (non-linear correlation h2) and time-frequency representation (Morlet wavelets). The software is freely available under the LGPL3 license.

COMPARISON WITH EXISTING METHODS:

AnyWave is designed as an open, highly extensible solution, with an architecture that permits rapid delivery of new techniques to end users.

CONCLUSIONS:

We have developed AnyWave software as an efficient neurophysiological data visualizer able to integrate state of the art techniques. AnyWave offers an interface well suited to the needs of clinical research and an architecture designed for integrating new tools. We expect this software to strengthen the collaboration between clinical neurophysiologists and researchers in biomedical engineering and signal processing.

KEYWORDS:

EEG; Electrophysiology; MATLAB; MEG; Multi-platform software; Python; Signal processing

PMID:
25614386
DOI:
10.1016/j.jneumeth.2015.01.017
[Indexed for MEDLINE]

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