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

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.

[1]  Guillaume Gibert,et al.  OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments , 2010, PRESENCE: Teleoperators and Virtual Environments.

[2]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[3]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[4]  Martin Luessi,et al.  MEG and EEG data analysis with MNE-Python , 2013, Front. Neuroinform..

[5]  Nicolas Fourcaud-Trocmé,et al.  OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework , 2008, Front. Neuroinform.

[6]  J Gotman,et al.  Computerized EEG monitoring. , 1999, Seminars in pediatric neurology.

[7]  Théodore Papadopoulo,et al.  Consensus Matching Pursuit for multi-trial EEG signals , 2009, Journal of Neuroscience Methods.

[8]  Karim Jerbi,et al.  ELAN: A Software Package for Analysis and Visualization of MEG, EEG, and LFP Signals , 2011, Comput. Intell. Neurosci..

[9]  Christoph M. Michel,et al.  Spatiotemporal Analysis of Multichannel EEG: CARTOOL , 2011, Comput. Intell. Neurosci..

[10]  Karl J. Friston,et al.  EEG and MEG Data Analysis in SPM8 , 2011, Comput. Intell. Neurosci..

[11]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[12]  F. Wendling,et al.  Clinical Engineering in Neurophysiology: an Object-oriented Platform for Signal Visualization and Processing , 1998 .

[13]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.

[14]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..