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      MEG and EEG data analysis with MNE-Python


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          Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.

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          Most cited references28

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          The NumPy array: a structure for efficient numerical computation

          In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.
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            Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

            Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
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              Identifying true brain interaction from EEG data using the imaginary part of coherency.

              The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. It is possible to reliably detect brain interaction during movement from EEG data. The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.

                Author and article information

                Front Neurosci
                Front Neurosci
                Front. Neuroinform.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                26 December 2013
                : 7
                [1] 1Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI Paris, France
                [2] 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School Charlestown MA, USA
                [3] 3NeuroSpin, CEA Saclay Gif-sur-Yvette, France
                [4] 4Institute for Learning and Brain Sciences, University of Washington Seattle WA, USA
                [5] 5Institute of Neuroscience and Medicine - Cognitive Neuroscience (INM-3) Forschungszentrum Juelich, Germany
                [6] 6Brain Imaging Lab, Department of Psychiatry, University Hospital Cologne, Germany
                [7] 7Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology Ilmenau, Germany
                [8] 8Department of Psychology, New York University New York, NY, USA
                [9] 9Psychological Imaging Laboratory, Psychology, School of Natural Sciences, University of Stirling Stirling, UK
                [10] 10Department of Biomedical Engineering and Computational Science, Aalto University School of Science Espoo, Finland
                [11] 11Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University School of Science Espoo, Finland
                Author notes

                Edited by: Satrajit S. Ghosh, Massachusetts Institute of Technology, USA

                Reviewed by: Samuel Garcia, Université Claude Bernard Lyon I, France; Forrest S. Bao, University of Akron, USA

                *Correspondence: Alexandre Gramfort, Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, 37-39 Rue Dareau, 75014 Paris, France e-mail: alexandre.gramfort@ 123456telecom-paristech.fr

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience.

                Copyright © 2013 Gramfort, Luessi, Larson, Engemann, Strohmeier, Brodbeck, Goj, Jas, Brooks, Parkkonen and Hämäläinen.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or 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.

                : 27 September 2013
                : 09 December 2013
                Page count
                Figures: 10, Tables: 5, Equations: 0, References: 56, Pages: 13, Words: 9409
                Methods Article

                electroencephalography (eeg),python,open-source,magnetoencephalography (meg),neuroimaging,software


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