PyTES: A Python toolbox for closed-loop transcranial electrical stimulation

Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-b...
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Autor*in:	Jiachen Xu [verfasserIn] Moritz Grosse-Wentrup [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Closed-loop system Transcranial electrical stimulation (TES) Personalized system Python toolbox

Übergeordnetes Werk:	In: SoftwareX - Elsevier, 2016, 23(2023), Seite 101403-
Übergeordnetes Werk:	volume:23 ; year:2023 ; pages:101403-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.softx.2023.101403

Katalog-ID:	DOAJ090573137

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publishDate	2023
allfields	10.1016/j.softx.2023.101403 doi (DE-627)DOAJ090573137 (DE-599)DOAJc08a13c7d71047ea936f86404c318bf6 DE-627 ger DE-627 rakwb eng QA76.75-76.765 Jiachen Xu verfasserin aut PyTES: A Python toolbox for closed-loop transcranial electrical stimulation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-based toolbox – PyTES – for closed-loop TES. PyTES is open-source, easy to integrate with existing Python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES. Closed-loop system Transcranial electrical stimulation (TES) Personalized system Python toolbox Computer software Moritz Grosse-Wentrup verfasserin aut In SoftwareX Elsevier, 2016 23(2023), Seite 101403- (DE-627)824451805 (DE-600)2819369-6 23527110 nnns volume:23 year:2023 pages:101403- https://doi.org/10.1016/j.softx.2023.101403 kostenfrei https://doaj.org/article/c08a13c7d71047ea936f86404c318bf6 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352711023000997 kostenfrei https://doaj.org/toc/2352-7110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 101403-
spelling	10.1016/j.softx.2023.101403 doi (DE-627)DOAJ090573137 (DE-599)DOAJc08a13c7d71047ea936f86404c318bf6 DE-627 ger DE-627 rakwb eng QA76.75-76.765 Jiachen Xu verfasserin aut PyTES: A Python toolbox for closed-loop transcranial electrical stimulation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-based toolbox – PyTES – for closed-loop TES. PyTES is open-source, easy to integrate with existing Python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES. Closed-loop system Transcranial electrical stimulation (TES) Personalized system Python toolbox Computer software Moritz Grosse-Wentrup verfasserin aut In SoftwareX Elsevier, 2016 23(2023), Seite 101403- (DE-627)824451805 (DE-600)2819369-6 23527110 nnns volume:23 year:2023 pages:101403- https://doi.org/10.1016/j.softx.2023.101403 kostenfrei https://doaj.org/article/c08a13c7d71047ea936f86404c318bf6 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352711023000997 kostenfrei https://doaj.org/toc/2352-7110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 101403-
allfields_unstemmed	10.1016/j.softx.2023.101403 doi (DE-627)DOAJ090573137 (DE-599)DOAJc08a13c7d71047ea936f86404c318bf6 DE-627 ger DE-627 rakwb eng QA76.75-76.765 Jiachen Xu verfasserin aut PyTES: A Python toolbox for closed-loop transcranial electrical stimulation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-based toolbox – PyTES – for closed-loop TES. PyTES is open-source, easy to integrate with existing Python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES. Closed-loop system Transcranial electrical stimulation (TES) Personalized system Python toolbox Computer software Moritz Grosse-Wentrup verfasserin aut In SoftwareX Elsevier, 2016 23(2023), Seite 101403- (DE-627)824451805 (DE-600)2819369-6 23527110 nnns volume:23 year:2023 pages:101403- https://doi.org/10.1016/j.softx.2023.101403 kostenfrei https://doaj.org/article/c08a13c7d71047ea936f86404c318bf6 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352711023000997 kostenfrei https://doaj.org/toc/2352-7110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 101403-
allfieldsGer	10.1016/j.softx.2023.101403 doi (DE-627)DOAJ090573137 (DE-599)DOAJc08a13c7d71047ea936f86404c318bf6 DE-627 ger DE-627 rakwb eng QA76.75-76.765 Jiachen Xu verfasserin aut PyTES: A Python toolbox for closed-loop transcranial electrical stimulation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-based toolbox – PyTES – for closed-loop TES. PyTES is open-source, easy to integrate with existing Python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES. Closed-loop system Transcranial electrical stimulation (TES) Personalized system Python toolbox Computer software Moritz Grosse-Wentrup verfasserin aut In SoftwareX Elsevier, 2016 23(2023), Seite 101403- (DE-627)824451805 (DE-600)2819369-6 23527110 nnns volume:23 year:2023 pages:101403- https://doi.org/10.1016/j.softx.2023.101403 kostenfrei https://doaj.org/article/c08a13c7d71047ea936f86404c318bf6 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352711023000997 kostenfrei https://doaj.org/toc/2352-7110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 101403-
allfieldsSound	10.1016/j.softx.2023.101403 doi (DE-627)DOAJ090573137 (DE-599)DOAJc08a13c7d71047ea936f86404c318bf6 DE-627 ger DE-627 rakwb eng QA76.75-76.765 Jiachen Xu verfasserin aut PyTES: A Python toolbox for closed-loop transcranial electrical stimulation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-based toolbox – PyTES – for closed-loop TES. PyTES is open-source, easy to integrate with existing Python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES. Closed-loop system Transcranial electrical stimulation (TES) Personalized system Python toolbox Computer software Moritz Grosse-Wentrup verfasserin aut In SoftwareX Elsevier, 2016 23(2023), Seite 101403- (DE-627)824451805 (DE-600)2819369-6 23527110 nnns volume:23 year:2023 pages:101403- https://doi.org/10.1016/j.softx.2023.101403 kostenfrei https://doaj.org/article/c08a13c7d71047ea936f86404c318bf6 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352711023000997 kostenfrei https://doaj.org/toc/2352-7110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 101403-
language	English
source	In SoftwareX 23(2023), Seite 101403- volume:23 year:2023 pages:101403-
sourceStr	In SoftwareX 23(2023), Seite 101403- volume:23 year:2023 pages:101403-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Closed-loop system Transcranial electrical stimulation (TES) Personalized system Python toolbox Computer software
isfreeaccess_bool	true
container_title	SoftwareX
authorswithroles_txt_mv	Jiachen Xu @@aut@@ Moritz Grosse-Wentrup @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	824451805
id	DOAJ090573137
language_de	englisch
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callnumber-first	Q - Science
author	Jiachen Xu
spellingShingle	Jiachen Xu misc QA76.75-76.765 misc Closed-loop system misc Transcranial electrical stimulation (TES) misc Personalized system misc Python toolbox misc Computer software PyTES: A Python toolbox for closed-loop transcranial electrical stimulation
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topic_title	QA76.75-76.765 PyTES: A Python toolbox for closed-loop transcranial electrical stimulation Closed-loop system Transcranial electrical stimulation (TES) Personalized system Python toolbox
topic	misc QA76.75-76.765 misc Closed-loop system misc Transcranial electrical stimulation (TES) misc Personalized system misc Python toolbox misc Computer software
topic_unstemmed	misc QA76.75-76.765 misc Closed-loop system misc Transcranial electrical stimulation (TES) misc Personalized system misc Python toolbox misc Computer software
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title	PyTES: A Python toolbox for closed-loop transcranial electrical stimulation
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title_full	PyTES: A Python toolbox for closed-loop transcranial electrical stimulation
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title_sort	pytes: a python toolbox for closed-loop transcranial electrical stimulation
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title_auth	PyTES: A Python toolbox for closed-loop transcranial electrical stimulation
abstract	Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-based toolbox – PyTES – for closed-loop TES. PyTES is open-source, easy to integrate with existing Python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES.
abstractGer	Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-based toolbox – PyTES – for closed-loop TES. PyTES is open-source, easy to integrate with existing Python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES.
abstract_unstemmed	Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a Python-based toolbox – PyTES – for closed-loop TES. PyTES is open-source, easy to integrate with existing Python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES.
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title_short	PyTES: A Python toolbox for closed-loop transcranial electrical stimulation
url	https://doi.org/10.1016/j.softx.2023.101403 https://doaj.org/article/c08a13c7d71047ea936f86404c318bf6 http://www.sciencedirect.com/science/article/pii/S2352711023000997 https://doaj.org/toc/2352-7110
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PyTES: A Python toolbox for closed-loop transcranial electrical stimulation

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