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      FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

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          Abstract

          This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

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          Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources.

          Cornelis Stam, Guido Nolte, Andreas Daffertshofer (2007)
          To address the problem of volume conduction and active reference electrodes in the assessment of functional connectivity, we propose a novel measure to quantify phase synchronization, the phase lag index (PLI), and compare its performance to the well-known phase coherence (PC), and to the imaginary component of coherency (IC). The PLI is a measure of the asymmetry of the distribution of phase differences between two signals. The performance of PLI, PC, and IC was examined in (i) a model of 64 globally coupled oscillators, (ii) an EEG with an absence seizure, (iii) an EEG data set of 15 Alzheimer patients and 13 control subjects, and (iv) two MEG data sets. PLI and PC were more sensitive than IC to increasing levels of true synchronization in the model. PC and IC were influenced stronger than PLI by spurious correlations because of common sources. All measures detected changes in synchronization during the absence seizure. In contrast to PC, PLI and IC were barely changed by the choice of different montages. PLI and IC were superior to PC in detecting changes in beta band connectivity in AD patients. Finally, PLI and IC revealed a different spatial pattern of functional connectivity in MEG data than PC. The PLI performed at least as well as the PC in detecting true changes in synchronization in model and real data but, at the same token and like-wise the IC, it was much less affected by the influence of common sources and active reference electrodes. Copyright 2007 Wiley-Liss, Inc.
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            Identifying true brain interaction from EEG data using the imaginary part of coherency.

            Guido Nolte, Ou Bai, Lewis A. Wheaton … (2004)
            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.
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              Measuring phase synchrony in brain signals

              Francisco J. Varela, Eugenio Rodriguez, Jean-Philippe Lachaux … (1999)
              This article presents, for the first time, a practical method for the direct quantification of frequency‐specific synchronization (i.e., transient phase‐locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long‐range neural integration during cognitive tasks. The method, called phase‐locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time‐resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large‐scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194–208, 1999. © 1999 Wiley‐Liss, Inc.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Intell Neurosci
                Journal ID (publisher-id): CIN
                Title: Computational Intelligence and Neuroscience
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1687-5265
                ISSN (Electronic): 1687-5273
                Publication date (Print): 2011
                Publication date (Electronic): 23 December 2010
                Volume: 2011
                Electronic Location Identifier: 156869
                Affiliations
                1Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, 6500 HB Nijmegen, The Netherlands
                2Ernst Strüngmann Institute and Max Planck Society, D-60528 Frankfurt, Germany
                Author notes

                Academic Editor: Sylvain Baillet

                Article
                DOI: 10.1155/2011/156869
                PMC ID: 3021840
                PubMed ID: 21253357
                SO-VID: 4d350485-c5c8-4c50-b2e1-b3779106796e
                Copyright © Copyright © 2011 Robert Oostenveld et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 26 August 2010
                Date accepted : 18 October 2010
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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