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MEG and EEG data analysis with MNE-Python
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
A two-way regularization method for MEG source reconstruction
The MEG inverse problem refers to the reconstruction of the neural activity
of the brain from magnetoencephalography (MEG) measurements. We propose a
two-way regularization (TWR) method to solve the MEG inverse problem under the
assumptions that only a small number of locations in space are responsible for
the measured signals (focality), and each source time course is smooth in time
(smoothness). The focality and smoothness of the reconstructed signals are
ensured respectively by imposing a sparsity-inducing penalty and a roughness
penalty in the data fitting criterion. A two-stage algorithm is developed for
fast computation, where a raw estimate of the source time course is obtained in
the first stage and then refined in the second stage by the two-way
regularization. The proposed method is shown to be effective on both synthetic
and real-world examples.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS531 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Review of analytical instruments for EEG analysis
Since it was first used in 1926, EEG has been one of the most useful
instruments of neuroscience. In order to start using EEG data we need not only
EEG apparatus, but also some analytical tools and skills to understand what our
data mean. This article describes several classical analytical tools and also
new one which appeared only several years ago. We hope it will be useful for
those researchers who have only started working in the field of cognitive EEG
An introduction to time-resolved decoding analysis for M/EEG
The human brain is constantly processing and integrating information in order
to make decisions and interact with the world, for tasks from recognizing a
familiar face to playing a game of tennis. These complex cognitive processes
require communication between large populations of neurons. The non-invasive
neuroimaging methods of electroencephalography (EEG) and magnetoencephalography
(MEG) provide population measures of neural activity with millisecond precision
that allow us to study the temporal dynamics of cognitive processes. However,
multi-sensor M/EEG data is inherently high dimensional, making it difficult to
parse important signal from noise. Multivariate pattern analysis (MVPA) or
"decoding" methods offer vast potential for understanding high-dimensional
M/EEG neural data. MVPA can be used to distinguish between different conditions
and map the time courses of various neural processes, from basic sensory
processing to high-level cognitive processes. In this chapter, we discuss the
practical aspects of performing decoding analyses on M/EEG data as well as the
limitations of the method, and then we discuss some applications for
understanding representational dynamics in the human brain
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