BioSig is an open source software library for biomedical signal processing. The aim
of the BioSig project is to foster research in biomedical signal processing by providing
free and open source software tools for many different application areas. Some of the
areas where BioSig can be employed are neuroinformatics, brain-computer interfaces,
neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover,
the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram
(ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG),
or respiration signals is a very relevant element of the BioSig project. Specifically,
BioSig provides solutions for data acquisition, artifact processing, quality control, feature
extraction, classification, modeling, and data visualization, to name a few. In this
paper, we highlight several methods to help students and researchers to work more efficiently
with biomedical signals.

Carmen Vidaurre

Tilmann H. Sander

Alois Schlögl

BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

Automatic estimation of current dipoles from biomagnetic data is
still a problematic task. This is due not only to the ill-posedness of
the inverse problem but also to two intrinsic difficulties introduced by
the dipolar model: the unknown number of sources and the nonlinear
relationship between the source locations and the data. Recently, we
have developed a new Bayesian approach, particle filtering, based on
dynamical tracking of the dipole constellation. Contrary to many
dipole-based methods, particle filtering does not assume stationarity
of the source configuration: the number of dipoles and their positions
are estimated and updated dynamically during the course of the MEG
sequence. We have now developed a Matlab-based graphical user interface,
which allows nonexpert users to do automatic dipole estimation
from MEG data with particle filtering. In the present paper, we describe
the main features of the software and show the analysis of both
a synthetic data set and an experimental dataset.

C. Campi

A. Pascarella

A. Sorrentino

M. Piana

Highly Automated Dipole EStimation (HADES)

SPM  is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

Vladimir Litvak

Jérémie Mattout

Stefan Kiebel

Christophe Phillips

Richard Henson

James Kilner

Gareth Barnes

Robert Oostenveld

Jean Daunizeau

Guillaume Flandin

Will Penny

Karl Friston

EEG and MEG Data Analysis in SPM8

Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses.

Cyril R. Pernet

Nicolas Chauveau

Carl Gaspar

Guillaume A. Rousselet

LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. 
Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. 
Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

Thomas Koenig

Mara Kottlow

Maria Stein

Lester Melie-Garc&#237;a

Ragu: A Free Tool for the Analysis of EEG and MEG Event-Related Scalp Field Data Using Global Randomization Statistics

This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way.

Denis Brunet

Micah M. Murray

Christoph M. Michel

Spatiotemporal Analysis of Multichannel EEG: CARTOOL

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.

Pascal Fries

Eric Maris

Jan-Mathijs Schoffelen

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

About this journal

Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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Chief Editor, Professor Cichocki, engages in world-leading research in the field of artificial intelligence and biomedical applications of advanced data analytics technologies.

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Computational Intelligence and Neuroscience - Table of contents 2011

Computational Intelligence and Neuroscience | Hindawi

title

Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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Computational Intelligence and Neuroscience

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Computational Intelligence and Neuroscience