Nonparametric statistical testing of EEG- and MEG-data
TL;DR: This paper forms a null hypothesis and shows that the nonparametric test controls the false alarm rate under this null hypothesis, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect.
About: This article is published in Journal of Neuroscience Methods.The article was published on 2007-08-15. It has received 6502 citations till now. The article focuses on the topics: Test statistic & p-value.
Citations
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TL;DR: FieldTrip is an open source software package that 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.
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.
7,963 citations
Cites methods from "Nonparametric statistical testing o..."
...Independent of the data representation, FieldTrip uses the same underlying code to assess significance using parametric or nonparametric algorithms [7] for statistical inference....
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TL;DR: ERPLAB adds to EEGLAB’s EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others.
Abstract: ERPLAB Toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB’s EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB’s tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user’s guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations.
1,726 citations
Cites methods from "Nonparametric statistical testing o..."
...Second, permutation-based approaches are becoming very popular in ERP research (Blair and Karniski, 1993; Maris and Oostenveld, 2007; Maris, 2012), and ERPLAB contains a permutation tool that makes it easy for users to permute the data in various ways....
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TL;DR: MNE-Python as discussed by the authors is an open-source software package that provides 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.
Abstract: 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.
1,723 citations
TL;DR: Detailed information about the MNE package is given and typical use cases are described while also warning about potential caveats in analysis.
1,447 citations
Cites background or methods from "Nonparametric statistical testing o..."
..., 2005), topological considerations (Maris and Oostenveld, 2007), and variance control (Ridgway et al....
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...…reason that statistical analysis methods for M/EEG are evolving, and different forms of non-parametric approaches (Nichols and Holmes, 2002; Pantazis et al., 2005), topological considerations (Maris and Oostenveld, 2007), and variance control (Ridgway et al., 2012) are being actively investigated....
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...The non-parametric statistical tests (Maris and Oostenveld, 2007) implemented in MNE are designed to provide a generic framework for statistical analysis....
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TL;DR: The weighted phase lag index (WPLI) as mentioned in this paper measures the contribution of the observed phase leads and lags by the magnitude of the imaginary component of the cross-spectrum.
1,041 citations
References
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TL;DR: The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described.
Abstract: Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices.
5,777 citations
TL;DR: In a sentence reading task, words that occurred out of context were associated with specific types of event-related brain potentials that elicited a late negative wave (N400).
Abstract: In a sentence reading task, words that occurred out of context were associated with specific types of event-related brain potentials. Words that were physically aberrant (larger than normal) elecited a late positive series of potentials, whereas semantically inappropriate words elicited a late negative wave (N400). The N400 wave may be an electrophysiological sign of the "reprocessing" of semantically anomalous information.
4,226 citations
03 Jun 1993
TL;DR: In this article, the authors present a bibliographical reference record created on 2004-09-07, modified on 2016-08-08, and includes references and indexes Reference Record.
Abstract: Note: Includes bibliographical references and indexes Reference Record created on 2004-09-07, modified on 2016-08-08
1,962 citations
TL;DR: Findings using an electrophysiological brain component, the N400, that reveal the nature and timing of semantic memory use during language comprehension support a view of memory in which world knowledge is distributed across multiple, plastic-yet-structured, largely modality-specific processing areas, and in which meaning is an emergent, temporally extended process.
1,924 citations
TL;DR: It is envisaged that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.
1,175 citations