578 research outputs found
Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression
Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award TR01-GM104948)National Institutes of Health (U.S.) (Grant R44NS071988)National Institute of Neurological Diseases and Stroke (U.S.) (Grant Grant R44NS071988
EEG-assisted retrospective motion correction for fMRI: E-REMCOR
We propose a method for retrospective motion correction of fMRI data in
simultaneous EEG-fMRI that employs the EEG array as a sensitive motion
detector. EEG motion artifacts are used to generate motion regressors
describing rotational head movements with millisecond temporal resolution.
These regressors are utilized for slice-specific motion correction of
unprocessed fMRI data. Performance of the method is demonstrated by correction
of fMRI data from five patients with major depressive disorder, who exhibited
head movements by 1-3 mm during a resting EEG-fMRI run. The fMRI datasets,
corrected using eight to ten EEG-based motion regressors, show significant
improvements in temporal SNR (TSNR) of fMRI time series, particularly in the
frontal brain regions and near the surface of the brain. The TSNR improvements
are as high as 50% for large brain areas in single-subject analysis and as high
as 25% when the results are averaged across the subjects. Simultaneous
application of the EEG-based motion correction and physiological noise
correction by means of RETROICOR leads to average TSNR enhancements as high as
35% for large brain regions. These TSNR improvements are largely preserved
after the subsequent fMRI volume registration and regression of fMRI motion
parameters. The proposed EEG-assisted method of retrospective fMRI motion
correction (referred to as E-REMCOR) can be used to improve quality of fMRI
data with severe motion artifacts and to reduce spurious correlations between
the EEG and fMRI data caused by head movements. It does not require any
specialized equipment beyond the standard EEG-fMRI instrumentation and can be
applied retrospectively to any existing EEG-fMRI data set.Comment: 19 pages, 10 figures, to appear in NeuroImag
Motion Artifact Processing Techniques for Physiological Signals
The combination of reducing birth rate and increasing life expectancy continues to drive
the demographic shift toward an ageing population and this is placing an ever-increasing
burden on our healthcare systems. The urgent need to address this so called healthcare
\time bomb" has led to a rapid growth in research into ubiquitous, pervasive and
distributed healthcare technologies where recent advances in signal acquisition, data
storage and communication are helping such systems become a reality. However, similar
to recordings performed in the hospital environment, artifacts continue to be a major
issue for these systems. The magnitude and frequency of artifacts can vary signicantly
depending on the recording environment with one of the major contributions due to
the motion of the subject or the recording transducer. As such, this thesis addresses
the challenges of the removal of this motion artifact removal from various physiological
signals.
The preliminary investigations focus on artifact identication and the tagging of physiological
signals streams with measures of signal quality. A new method for quantifying
signal quality is developed based on the use of inexpensive accelerometers which facilitates
the appropriate use of artifact processing methods as needed. These artifact
processing methods are thoroughly examined as part of a comprehensive review of the
most commonly applicable methods. This review forms the basis for the comparative
studies subsequently presented. Then, a simple but novel experimental methodology
for the comparison of artifact processing techniques is proposed, designed and tested
for algorithm evaluation. The method is demonstrated to be highly eective for the
type of artifact challenges common in a connected health setting, particularly those concerned
with brain activity monitoring. This research primarily focuses on applying the
techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography
(EEG) data due to their high susceptibility to contamination by subject motion related
artifact.
Using the novel experimental methodology, complemented with simulated data, a comprehensive
comparison of a range of artifact processing methods is conducted, allowing
the identication of the set of the best performing methods. A novel artifact removal
technique is also developed, namely ensemble empirical mode decomposition with canonical
correlation analysis (EEMD-CCA), which provides the best results when applied on
fNIRS data under particular conditions. Four of the best performing techniques were
then tested on real ambulatory EEG data contaminated with movement artifacts comparable
to those observed during in-home monitoring.
It was determined that when analysing EEG data, the Wiener lter is consistently
the best performing artifact removal technique. However, when employing the fNIRS
data, the best technique depends on a number of factors including: 1) the availability
of a reference signal and 2) whether or not the form of the artifact is known. It is
envisaged that the use of physiological signal monitoring for patient healthcare will grow
signicantly over the next number of decades and it is hoped that this thesis will aid in
the progression and development of artifact removal techniques capable of supporting
this growth
Independent EEG Sources Are Dipolar
Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison)
Detection of Human Vigilance State During Locomotion Using Wearable FNIRS
Human vigilance is a cognitive function that requires sustained attention toward change in the environment. Human vigilance detection is a widely investigated topic which can be accomplished by various approaches. Most studies have focused on stationary vigilance detection due to the high effect of interference such as motion artifacts which are prominent in common movements such as walking. Functional Near-Infrared Spectroscopy is a preferred modality in vigilance detection due to the safe nature, the low cost and ease of implementation. fNIRS is not immune to motion artifact interference, and therefore human vigilance detection performance would be severely degraded when studied during locomotion. Properly treating and removing walking-induced motion artifacts from the contaminated signals is crucial to ensure accurate vigilance detection. This study compared the vigilance level detection during both stationary and walking states and confirmed that the performance of vigilance level detection during walking is significantly deteriorated (with a
Advanced Pipelines For Artifact Removal From EEG Data
Feature extraction and working with EEG data has become one the most
challenging studies these years. The raw EEG signal has various artifacts and
needs to be detected and separated from brain components. This study is part of
ERC. For removing artifacts from EEG data , this procedure done by a method
known as “semi-automatic ICs selection pipeline”.This method was developed
and verified by Cosynclab directed by Prof. Betti in Rome (where I spent my
internship). In particular, the thesis work aims to investigate another method for
complementing semi-automatic ICs selection pipeline and evaluate results
which conveys to increasing the accuracy of semi-automatic ICs selection
pipeline.The ICA algorithm derives independent sources from highly correlated
EEG signals statistically without concern for the actual location or configuration
of the EEG signal source . It is used to locate concurrent signal sources that are
either too close together or too broadly scattered to be separated using
conventional localization techniques. The primary issue in understanding ICA
output is determining the right dimension of the input channels and the
physiological and/or psychophysiological relevance of the resulting ICA source
channels.With semi-automatic ICs selection pipeline method more than 2600
ICs evaluated and 405 ICs labeled as brains and the rest classified as artifacts.
To evaluate these 405 ICs and increase possible accuracy another method was
used known as ICLabel. ICLabel projects had been proposed by EEGLAB. This
is a method based on Deep Learning and provides classification based on EEG
IC classifier1
. After running and comparing the two methods pipeline, then,we
designed an application for comparison and visualization output for both
methods which name is IC selection.With this application we realize some
modification needed for future steps for labeling with semi-automatic ICs
selection pipeline method and some artifacts could change from artifacts to
brain.Feature extraction and working with EEG data has become one the most
challenging studies these years. The raw EEG signal has various artifacts and
needs to be detected and separated from brain components. This study is part of
ERC. For removing artifacts from EEG data , this procedure done by a method
known as “semi-automatic ICs selection pipeline”.This method was developed
and verified by Cosynclab directed by Prof. Betti in Rome (where I spent my
internship). In particular, the thesis work aims to investigate another method for
complementing semi-automatic ICs selection pipeline and evaluate results
which conveys to increasing the accuracy of semi-automatic ICs selection
pipeline.The ICA algorithm derives independent sources from highly correlated
EEG signals statistically without concern for the actual location or configuration
of the EEG signal source . It is used to locate concurrent signal sources that are
either too close together or too broadly scattered to be separated using
conventional localization techniques. The primary issue in understanding ICA
output is determining the right dimension of the input channels and the
physiological and/or psychophysiological relevance of the resulting ICA source
channels.With semi-automatic ICs selection pipeline method more than 2600
ICs evaluated and 405 ICs labeled as brains and the rest classified as artifacts.
To evaluate these 405 ICs and increase possible accuracy another method was
used known as ICLabel. ICLabel projects had been proposed by EEGLAB. This
is a method based on Deep Learning and provides classification based on EEG
IC classifier1
. After running and comparing the two methods pipeline, then,we
designed an application for comparison and visualization output for both
methods which name is IC selection.With this application we realize some
modification needed for future steps for labeling with semi-automatic ICs
selection pipeline method and some artifacts could change from artifacts to
brain
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