Objective: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating ...electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data. Methods: This paper systematically evaluates ASR on 20 EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR. Results: ASR removes more eye and muscle components than brain components. Even though some eye and muscle components retain after ASR cleaning, the power of their temporal activities is reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition. Conclusions: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities. Significance: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.
The ICLabel dataset is comprised of training and test sets of a set of spatiotemporal features of electroencephalographic (EEG) independent components (IC). The ICLabel training set feature sets were ...computed for over 200,000 EEG ICs from more than 6,000 existing EEG recordings. More than 8,000 of these ICs have accompanying crowdsourced IC labels across seven IC categories: Brain, Muscle, Eye, Heart, Line Nosie, Channel Noise, and Other. The feature-sets included in the ICLabel dataset are scalp topography images, channel-based scalp topography measures, power spectral densities (PSD) measures (median, variance and kurtosis) and autocorrelation functions, equivalent current dipole (ECD) model fits for single and bilaterally symmetric dipole models, plus features used in several published IC classifier approaches. The ICLabel test set is comprised of 130 ICs from 10 datasets not included in the training set. Each of the test set ICs has an associated IC label estimated based on labels provided by six ICA-EEG experts. Files necessary for adding to and amending the dataset are also included, plus a python class containing useful methods for interacting with the dataset, and IC classifications produced by several existing IC classifiers. These data are linked to the article, “ICLabel: An automated electroencephalographic independent component classifier, dataset, and website” 1. An active tutorial and crowdsourcing website is available: iclabel.ucsd.edu/tutorial/overview.
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 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) the ICLabel dataset containing spatiotemporal measures for over 200,000 ICs from more than 6000 EEG recordings and matching component labels for over 6000 of those ICs, all using common average reference, (2) the ICLabel website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier, freely available for MATLAB. The ICLabel classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The classifier outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories while computing those labels ten times faster than that classifier as shown by a systematic comparison against other publicly available EEG IC classifiers.
•We present ICLabel: an EEG independent component classifier, dataset, and website.•The classifier offers state-of-the-art performance, 13x faster than the next best.•The classifier is trained on crowdsourced labels collected from iclabel.ucsd.edu.•The classifier, website, and dataset are all freely and publicly available.
One of the greatest challenges that hinder the decoding and application of electroencephalography (EEG) is that EEG recordings almost always contain artifacts - non-brain signals. Among existing ...automatic artifact-removal methods, artifact subspace reconstruction (ASR) is an online and real-time capable, component-based method that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameter have not been evaluated and reported, especially on real EEG data. This study systematically validates ASR on ten EEG recordings in a simulated driving experiment. Independent component analysis (ICA) is applied to separate artifacts from brain signals to allow a quantitative assessment of ASR's effectiveness in removing various types of artifacts and preserving brain activities. Empirical results show that the optimal ASR parameter is between 10 and 100, which is small enough to remove activities from artifacts and eye-related components and large enough to retain signals from brain-related components. With the appropriate choice of the parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.
Crowd labeling latent Dirichlet allocation Pion-Tonachini, Luca; Makeig, Scott; Kreutz-Delgado, Ken
Knowledge and information systems,
12/2017, Letnik:
53, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Large, unlabeled datasets are abundant nowadays, but getting labels for those datasets can be expensive and time-consuming. Crowd labeling is a crowdsourcing approach for gathering such labels from ...workers whose suggestions are not always accurate. While a variety of algorithms exist for this purpose, we present crowd labeling latent Dirichlet allocation (CL-LDA), a generalization of latent Dirichlet allocation that can solve a more general set of crowd labeling problems. We show that it performs as well as other methods and at times better on a variety of simulated and actual datasets while treating each label as compositional rather than indicating a discrete class. In addition, prior knowledge of workers’ abilities can be incorporated into the model through a structured Bayesian framework. We then apply CL-LDA to the EEG independent component labeling dataset, using its generalizations to further explore the utility of the algorithm. We discuss prospects for creating classifiers from the generated labels.
There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This ...non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6–13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.
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•Provides unsupervised soft segmentation of electroencephalographic (EEG) data.•Uses multiple mixtures of independent sources to model EEG data non-stationarity.•Interpretable results on simulations and actual sleep and drowsy driving data.•Decomposition usable for sleep-stage classification and drowsiness estimation.•Characterizes subject cognitive state and transitions with high temporal resolution.
We demonstrated an optical coding method to measure the position of each particle in a microfluidic channel. The technique utilizes a specially designed pattern as a spatial mask to encode the ...forward scattering signal of each particle. From the waveform of the forward scattering signal, one can obtain the information about the particle position and velocity. The technique enables us to experimentally investigate the complex relations between particle positions within the microfluidic channel and flow conditions and particle sizes. The method also produces insight for important phenomenon in microfluidic and lab-on-a-chip devices such as inertial focusing, Dean flow, flow confinement, etc.
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. While ICA-based methods have been seeing more and more use, EEG researchers are hampered by the additional manual intervention necessary for source-resolved analyses. These issues can be largely mitigated through the automation of several stages of EEG source analysis. To this end, we developed and evaluated the ICLabel classifier, an automated independent component classifier trained on a large dataset with crowdsourced labels. The crowdsourced labels were estimated using the novel crowd labeling (CL) algorithm, crowd labeling latent Dirichlet allocation (CL-LDA), developed here. The ICLabel dataset that was used to train the ICLabel classifier was also made public to aid in future development of IC classifiers. We also evaluated artifact subspace reconstruction (ASR), an algorithm for artifact removal which is applicable both offline and in real-time, and aids both channel-level and source-level analyses. These tools are combined in the Real-time EEG Source-mapping toolbox (REST) to showcase the utility and ease of real-time, source-level analyses once the individual components of an EEG analysis pipeline are automated. Finally we evaluate adaptive mixture ICA (AMICA) and explore its utility for automatic EEG segmentation and nonstationary analysis. All of these tools and methods are open-source and freely available online.
An “optical space-time coding method” was applied to microfluidic devices to detect the forward and large angle light scattering signals for unlabelled bead and cell detection. Because of the ...enhanced sensitivity by this method, silicon pin photoreceivers can be used to detect both forward scattering (FS) and large angle (45–60°) scattering (LAS) signals, the latter of which has been traditionally detected by a photomultiplier tube. This method yields significant improvements in coefficients of variation (CV), producing CVs of 3.95% to 10.05% for FS and 7.97% to 26.12% for LAS with 15 μm, 10 μm, and 5 μm beads. These are among the best values ever demonstrated with microfluidic devices. The optical space-time coding method also enables us to measure the speed and position of each particle, producing valuable information for the design and assessment of microfluidic lab-on-a-chip devices such as flow cytometers and complete blood count devices.
Non-brain contributions to electroencephalographic (EEG) signals, often referred to as artifacts, can hamper the analysis of scalp EEG recordings. This is especially true when artifacts have large ...amplitudes (e.g., movement artifacts), or occur continuously (like eye-movement artifacts). Offline automated pipelines can detect and reduce artifact in EEG data, but no good solution exists for online processing of EEG data in near real time. Here, we propose the combined use of online artifact subspace reconstruction (ASR) to remove large amplitude transients, and online recursive independent component analysis (ORICA) combined with an independent component (IC) classifier to compute, classify, and remove artifact ICs. We demonstrate the efficacy of the proposed pipeline using 2 EEG recordings containing series of (1) movement and muscle artifacts, and (2) cued blinks and saccades. This pipeline is freely available in the Real-time EEG Source-mapping Toolbox (REST) for MATLAB (The Mathworks, Inc.).
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