Neo: an object model for handling electrophysiology data in multiple formats

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis metho...

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Published in:	Frontiers in neuroinformatics Vol. 8; p. 10
Main Authors:	Garcia, Samuel, Guarino, Domenico, Jaillet, Florent, Jennings, Todd, Pröpper, Robert, Rautenberg, Philipp L., Rodgers, Chris C., Sobolev, Andrey, Wachtler, Thomas, Yger, Pierre, Davison, Andrew P.
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Research Foundation 20.02.2014 Frontiers Media Frontiers Media S.A
Subjects:	Application programming interface Computer programs Computer Science Consortia Data analysis Data processing EEG Electroencephalography Electrophysiology File formats Information sharing Interoperability Language Libraries Life Sciences Metadata Neurons and Cognition Neurophysiology Neuroscience Neurosciences python Signal and Image Processing Software Time series France Germany interoperability electrophysiology software file formats Python
ISSN:	1662-5196, 1662-5196
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Abstract	Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named "Neo," suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.
AbstractList	Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named "Neo," suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology. Neuroscientists use many different software tools to acquire, analyse and visualise electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs.A common representation of the core data would improve interoperability and facilitate data-sharing.To that end, we propose here a language-independent object model, named "Neo", suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language.In addition to representing electrophysiology data in memory for the purposes of analysis and visualisation, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats.Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB.Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation.For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualisation.Software for neurophysiology data analysis and visualisation built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology. Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named "Neo," suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named "Neo," suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.
Author	Davison, Andrew P. Rodgers, Chris C. Jaillet, Florent Guarino, Domenico Sobolev, Andrey Yger, Pierre Rautenberg, Philipp L. Garcia, Samuel Pröpper, Robert Wachtler, Thomas Jennings, Todd
AuthorAffiliation	1 Centre de Recherche en Neuroscience de Lyon, CNRS UMR5292–INSERM U1028–Université Claude Bernard Lyon 1 Lyon, France 7 Helen Wills Neuroscience Institute, University of California Berkeley, CA, USA 4 Division of Neurobiology, Department Biology II, Ludwig-Maximilians-Universität München Planegg-Martinsried, Germany 3 Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS Marseille, France 6 G-Node, Department Biology II, Ludwig-Maximilians-Universität München Planegg-Martinsried, Germany 5 Neural Information Processing Group, TU Berlin Berlin, Germany 2 Unité de Neurosciences, Information et Complexité, Neuroinformatics group, CNRS UPR 3293 Gif-sur-Yvette, France
AuthorAffiliation_xml	– name: 5 Neural Information Processing Group, TU Berlin Berlin, Germany – name: 7 Helen Wills Neuroscience Institute, University of California Berkeley, CA, USA – name: 6 G-Node, Department Biology II, Ludwig-Maximilians-Universität München Planegg-Martinsried, Germany – name: 3 Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS Marseille, France – name: 4 Division of Neurobiology, Department Biology II, Ludwig-Maximilians-Universität München Planegg-Martinsried, Germany – name: 1 Centre de Recherche en Neuroscience de Lyon, CNRS UMR5292–INSERM U1028–Université Claude Bernard Lyon 1 Lyon, France – name: 2 Unité de Neurosciences, Information et Complexité, Neuroinformatics group, CNRS UPR 3293 Gif-sur-Yvette, France
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Copyright	2014. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License Copyright © 2014 Garcia, Guarino, Jaillet, Jennings, Pröpper, Rautenberg, Rodgers, Sobolev, Wachtler, Yger and Davison. 2014
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Snippet	Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file... Neuroscientists use many different software tools to acquire, analyse and visualise electrophysiological signals. However, incompatible data models and file...
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SubjectTerms	Application programming interface Computer programs Computer Science Consortia Data analysis Data processing EEG Electroencephalography Electrophysiology File formats Information sharing Interoperability Language Libraries Life Sciences Metadata Neurons and Cognition Neurophysiology Neuroscience Neurosciences python Signal and Image Processing Software Time series
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Title	Neo: an object model for handling electrophysiology data in multiple formats
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