2,523 research outputs found
Modeling sparse connectivity between underlying brain sources for EEG/MEG
We propose a novel technique to assess functional brain connectivity in
EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA),
can overcome the problem of volume conduction by modeling neural data
innovatively with the following ingredients: (a) the EEG is assumed to be a
linear mixture of correlated sources following a multivariate autoregressive
(MVAR) model, (b) the demixing is estimated jointly with the source MVAR
parameters, (c) overfitting is avoided by using the Group Lasso penalty. This
approach allows to extract the appropriate level cross-talk between the
extracted sources and in this manner we obtain a sparse data-driven model of
functional connectivity. We demonstrate the usefulness of SCSA with simulated
data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure
Analytical methods and experimental approaches for electrophysiological studies of brain oscillations
Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour
Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces
We propose a fusion approach that combines features from simultaneously
recorded electroencephalographic (EEG) and magnetoencephalographic (MEG)
signals to improve classification performances in motor imagery-based
brain-computer interfaces (BCIs). We applied our approach to a group of 15
healthy subjects and found a significant classification performance enhancement
as compared to standard single-modality approaches in the alpha and beta bands.
Taken together, our findings demonstrate the advantage of considering
multimodal approaches as complementary tools for improving the impact of
non-invasive BCIs
MERLiN: Mixture Effect Recovery in Linear Networks
Causal inference concerns the identification of cause-effect relationships
between variables, e.g. establishing whether a stimulus affects activity in a
certain brain region. The observed variables themselves often do not constitute
meaningful causal variables, however, and linear combinations need to be
considered. In electroencephalographic studies, for example, one is not
interested in establishing cause-effect relationships between electrode signals
(the observed variables), but rather between cortical signals (the causal
variables) which can be recovered as linear combinations of electrode signals.
We introduce MERLiN (Mixture Effect Recovery in Linear Networks), a family of
causal inference algorithms that implement a novel means of constructing causal
variables from non-causal variables. We demonstrate through application to EEG
data how the basic MERLiN algorithm can be extended for application to
different (neuroimaging) data modalities. Given an observed linear mixture, the
algorithms can recover a causal variable that is a linear effect of another
given variable. That is, MERLiN allows us to recover a cortical signal that is
affected by activity in a certain brain region, while not being a direct effect
of the stimulus. The Python/Matlab implementation for all presented algorithms
is available on https://github.com/sweichwald/MERLi
Efficient transfer entropy analysis of non-stationary neural time series
Information theory allows us to investigate information processing in neural
systems in terms of information transfer, storage and modification. Especially
the measure of information transfer, transfer entropy, has seen a dramatic
surge of interest in neuroscience. Estimating transfer entropy from two
processes requires the observation of multiple realizations of these processes
to estimate associated probability density functions. To obtain these
observations, available estimators assume stationarity of processes to allow
pooling of observations over time. This assumption however, is a major obstacle
to the application of these estimators in neuroscience as observed processes
are often non-stationary. As a solution, Gomez-Herrero and colleagues
theoretically showed that the stationarity assumption may be avoided by
estimating transfer entropy from an ensemble of realizations. Such an ensemble
is often readily available in neuroscience experiments in the form of
experimental trials. Thus, in this work we combine the ensemble method with a
recently proposed transfer entropy estimator to make transfer entropy
estimation applicable to non-stationary time series. We present an efficient
implementation of the approach that deals with the increased computational
demand of the ensemble method's practical application. In particular, we use a
massively parallel implementation for a graphics processing unit to handle the
computationally most heavy aspects of the ensemble method. We test the
performance and robustness of our implementation on data from simulated
stochastic processes and demonstrate the method's applicability to
magnetoencephalographic data. While we mainly evaluate the proposed method for
neuroscientific data, we expect it to be applicable in a variety of fields that
are concerned with the analysis of information transfer in complex biological,
social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON
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EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI
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