6,027 research outputs found
BMICA-independent component analysis based on B-spline mutual information estimator
The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis)
exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.Comment: Codes are available at:
https://sites.google.com/site/researchbyzhang/stsb
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
The electroencephalogram (EEG) provides a non-invasive, minimally
restrictive, and relatively low cost measure of mesoscale brain dynamics with
high temporal resolution. Although signals recorded in parallel by multiple,
near-adjacent EEG scalp electrode channels are highly-correlated and combine
signals from many different sources, biological and non-biological, independent
component analysis (ICA) has been shown to isolate the various source generator
processes underlying those recordings. Independent components (IC) found by ICA
decomposition can be manually inspected, selected, and interpreted, but doing
so requires both time and practice as ICs have no particular order or intrinsic
interpretations and therefore require further study of their properties.
Alternatively, sufficiently-accurate automated IC classifiers can be used to
classify ICs into broad source categories, speeding the analysis of EEG studies
with many subjects and enabling the use of ICA decomposition in near-real-time
applications. While many such classifiers have been proposed recently, this
work presents the ICLabel project comprised of (1) an IC dataset containing
spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG
recordings, (2) a website for collecting crowdsourced IC labels and educating
EEG researchers and practitioners about IC interpretation, and (3) the
automated ICLabel classifier. The classifier improves upon existing methods in
two ways: by improving the accuracy of the computed label estimates and by
enhancing its computational efficiency. The ICLabel classifier outperforms or
performs comparably to the previous best publicly available method for all
measured IC categories while computing those labels ten times faster than that
classifier as shown in a rigorous comparison against all other publicly
available EEG IC classifiers.Comment: Intended for NeuroImage. Updated from version one with minor
editorial and figure change
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Riemannian geometry has been successfully used in many brain-computer
interface (BCI) classification problems and demonstrated superior performance.
In this paper, for the first time, it is applied to BCI regression problems, an
important category of BCI applications. More specifically, we propose a new
feature extraction approach for Electroencephalogram (EEG) based BCI regression
problems: a spatial filter is first used to increase the signal quality of the
EEG trials and also to reduce the dimensionality of the covariance matrices,
and then Riemannian tangent space features are extracted. We validate the
performance of the proposed approach in reaction time estimation from EEG
signals measured in a large-scale sustained-attention psychomotor vigilance
task, and show that compared with the traditional powerband features, the
tangent space features can reduce the root mean square estimation error by
4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291
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