Cargando…
TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation
Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signa...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054290/ https://www.ncbi.nlm.nih.gov/pubmed/27774054 http://dx.doi.org/10.3389/fncir.2016.00078 |
_version_ | 1782458568291647488 |
---|---|
author | Atluri, Sravya Frehlich, Matthew Mei, Ye Garcia Dominguez, Luis Rogasch, Nigel C. Wong, Willy Daskalakis, Zafiris J. Farzan, Faranak |
author_facet | Atluri, Sravya Frehlich, Matthew Mei, Ye Garcia Dominguez, Luis Rogasch, Nigel C. Wong, Willy Daskalakis, Zafiris J. Farzan, Faranak |
author_sort | Atluri, Sravya |
collection | PubMed |
description | Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research. |
format | Online Article Text |
id | pubmed-5054290 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50542902016-10-21 TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation Atluri, Sravya Frehlich, Matthew Mei, Ye Garcia Dominguez, Luis Rogasch, Nigel C. Wong, Willy Daskalakis, Zafiris J. Farzan, Faranak Front Neural Circuits Neuroscience Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research. Frontiers Media S.A. 2016-10-07 /pmc/articles/PMC5054290/ /pubmed/27774054 http://dx.doi.org/10.3389/fncir.2016.00078 Text en Copyright © 2016 Atluri, Frehlich, Mei, Garcia Dominguez, Rogasch, Wong, Daskalakis and Farzan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Atluri, Sravya Frehlich, Matthew Mei, Ye Garcia Dominguez, Luis Rogasch, Nigel C. Wong, Willy Daskalakis, Zafiris J. Farzan, Faranak TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation |
title | TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation |
title_full | TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation |
title_fullStr | TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation |
title_full_unstemmed | TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation |
title_short | TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation |
title_sort | tmseeg: a matlab-based graphical user interface for processing electrophysiological signals during transcranial magnetic stimulation |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054290/ https://www.ncbi.nlm.nih.gov/pubmed/27774054 http://dx.doi.org/10.3389/fncir.2016.00078 |
work_keys_str_mv | AT atlurisravya tmseegamatlabbasedgraphicaluserinterfaceforprocessingelectrophysiologicalsignalsduringtranscranialmagneticstimulation AT frehlichmatthew tmseegamatlabbasedgraphicaluserinterfaceforprocessingelectrophysiologicalsignalsduringtranscranialmagneticstimulation AT meiye tmseegamatlabbasedgraphicaluserinterfaceforprocessingelectrophysiologicalsignalsduringtranscranialmagneticstimulation AT garciadominguezluis tmseegamatlabbasedgraphicaluserinterfaceforprocessingelectrophysiologicalsignalsduringtranscranialmagneticstimulation AT rogaschnigelc tmseegamatlabbasedgraphicaluserinterfaceforprocessingelectrophysiologicalsignalsduringtranscranialmagneticstimulation AT wongwilly tmseegamatlabbasedgraphicaluserinterfaceforprocessingelectrophysiologicalsignalsduringtranscranialmagneticstimulation AT daskalakiszafirisj tmseegamatlabbasedgraphicaluserinterfaceforprocessingelectrophysiologicalsignalsduringtranscranialmagneticstimulation AT farzanfaranak tmseegamatlabbasedgraphicaluserinterfaceforprocessingelectrophysiologicalsignalsduringtranscranialmagneticstimulation |