18,059 research outputs found
Covariance-domain Dictionary Learning for Overcomplete EEG Source Identification
We propose an algorithm targeting the identification of more sources than
channels for electroencephalography (EEG). Our overcomplete source
identification algorithm, Cov-DL, leverages dictionary learning methods applied
in the covariance-domain. Assuming that EEG sources are uncorrelated within
moving time-windows and the scalp mixing is linear, the forward problem can be
transferred to the covariance domain which has higher dimensionality than the
original EEG channel domain. This allows for learning the overcomplete mixing
matrix that generates the scalp EEG even when there may be more sources than
sensors active at any time segment, i.e. when there are non-sparse sources.
This is contrary to straight-forward dictionary learning methods that are based
on the assumption of sparsity, which is not a satisfied condition in the case
of low-density EEG systems. We present two different learning strategies for
Cov-DL, determined by the size of the target mixing matrix. We demonstrate that
Cov-DL outperforms existing overcomplete ICA algorithms under various scenarios
of EEG simulations and real EEG experiments
Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing
We discuss Bayesian analysis of multivariate time series with dynamic factor
models that exploit time-adaptive sparsity in model parametrizations via the
latent threshold approach. One central focus is on the transfer responses of
multiple interrelated series to underlying, dynamic latent factor processes.
Structured priors on model hyper-parameters are key to the efficacy of dynamic
latent thresholding, and MCMC-based computation enables model fitting and
analysis. A detailed case study of electroencephalographic (EEG) data from
experimental psychiatry highlights the use of latent threshold extensions of
time-varying vector autoregressive and factor models. This study explores a
class of dynamic transfer response factor models, extending prior Bayesian
modeling of multiple EEG series and highlighting the practical utility of the
latent thresholding concept in multivariate, non-stationary time series
analysis.Comment: 27 pages, 13 figures, link to external web site for supplementary
animated figure
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is
an early predictor of Parkinson's disease. This study proposes a
fully-automated framework for RBD detection consisting of automated sleep
staging followed by RBD identification. Analysis was assessed using a limited
polysomnography montage from 53 participants with RBD and 53 age-matched
healthy controls. Sleep stage classification was achieved using a Random Forest
(RF) classifier and 156 features extracted from electroencephalogram (EEG),
electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a
RF classifier was trained combining established techniques to quantify muscle
atonia with additional features that incorporate sleep architecture and the EMG
fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's
Kappa score. RBD detection accuracy improved by 10% to 96% (compared to
individual established metrics) when using manually annotated sleep staging.
Accuracy remained high (92%) when using automated sleep staging. This study
outperforms established metrics and demonstrates that incorporating sleep
architecture and sleep stage transitions can benefit RBD detection. This study
also achieved automated sleep staging with a level of accuracy comparable to
manual annotation. This study validates a tractable, fully-automated, and
sensitive pipeline for RBD identification that could be translated to wearable
take-home technology.Comment: 20 pages, 3 figure
A Novel Long-term, Multi-Channel and Non-invasive Electrophysiology Platform for Zebrafish.
Zebrafish are a popular vertebrate model for human neurological disorders and drug discovery. Although fecundity, breeding convenience, genetic homology and optical transparency have been key advantages, laborious and invasive procedures are required for electrophysiological studies. Using an electrode-integrated microfluidic system, here we demonstrate a novel multichannel electrophysiology unit to record multiple zebrafish. This platform allows spontaneous alignment of zebrafish and maintains, over days, close contact between head and multiple surface electrodes, enabling non-invasive long-term electroencephalographic recording. First, we demonstrate that electrographic seizure events, induced by pentylenetetrazole, can be reliably distinguished from eye or tail movement artifacts, and quantifiably identified with our unique algorithm. Second, we show long-term monitoring during epileptogenic progression in a scn1lab mutant recapitulating human Dravet syndrome. Third, we provide an example of cross-over pharmacology antiepileptic drug testing. Such promising features of this integrated microfluidic platform will greatly facilitate high-throughput drug screening and electrophysiological characterization of epileptic zebrafish
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