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.

/pdf/fieldtrip-open-source-software-for-advanced-analysis-of-meg-1cxn1l5h6y.pdf

FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

The WU-Minn Human Connectome Project: An Overview

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.

/pdf/meg-and-eeg-data-analysis-with-mne-python-2ix34apb7o.pdf

MEG and EEG data analysis with MNE-Python

There has been tremendous advances in our ability to produce images of human brain function. Applications of functional brain imaging extend from improving our understanding of the basic mechanisms of cognitive processes to better characterization of pathologies that impair normal function. Magnetoencephalography (MEG) and electroencephalography (EEG) (MEG/EEG) localize neural electrical activity using noninvasive measurements of external electromagnetic signals. Among the available functional imaging techniques, MEG and EEG uniquely have temporal resolutions below 100 ms. This temporal precision allows us to explore the timing of basic neural processes at the level of cell assemblies. MEG/EEG source localization draws on a wide range of signal processing techniques including digital filtering, three-dimensional image analysis, array signal processing, image modeling and reconstruction, and, blind source separation and phase synchrony estimation. We describe the underlying models currently used in MEG/EEG source estimation and describe the various signal processing steps required to compute these sources. In particular we describe methods for computing the forward fields for known source distributions and parametric and imaging-based approaches to the inverse problem.

Electromagnetic brain mapping

http://web.mit.edu/~ppurdon/www/source-imaging.pdf

EEG source imaging

A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map. The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models. The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach.

Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

A single-component pressure-sensitive paint (PC-PSP) based on a platinum complex was developed. This sprayable, fast-response PSP exhibited a short-response time, high robustness, and high-pressure sensitivity and was subsequently applied to the time-resolved measurement of pressure distribution. The binder of the proposed PC-PSP was prepared by mixing poly(isobutyl methacrylate) with titanium dioxide (TiO2) particles in various proportions. The effects of a particle mass content on the properties of the PSP, including response time, pressure sensitivity, and luminescent intensity, were investigated. The effects of applying hydrophilic and hydrophobic surface treatments to the TiO2 particles on the structure and mechanical robustness of the binder layer were also studied. A PC-PSP having practical applications was produced using hydrophobic TiO2. This material is straightforward to apply and shows strong adhesion to the substrate together with a fast response. Hydrophobic TiO2 particles, which had a polarity similar to that of the toluene used to make the binder, provided better adhesion because they could maintain a dense binder structure even when the particle mass content was increased to 97 wt%. A particle mass content of 93 wt% gave a high performance from the point of view of response time and roughness. 

A. Suzuki

Papers

Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

A platinum-based, fast-response, pressure-sensitive paint containing hydrophobic titanium dioxide