The Experts below are selected from a list of 3669 Experts worldwide ranked by ideXlab platform
Scott Makeig - One of the best experts on this subject based on the ideXlab platform.
-
The open EEGLAB portal Interface: High-Performance computing with EEGLAB.
NeuroImage, 2020Co-Authors: Ramón Martínez-cancino, Arnaud Delorme, Dung Truong, Fiorenzo Artoni, Kenneth Kreutz-delgado, Subhashini Sivagnanam, Kenneth Yoshimoto, Amitava Majumdar, Scott MakeigAbstract:Abstract EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently ( Delorme et al., 2019 ) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.
-
The Open EEGLAB portal
2019Co-Authors: Arnaud Delorme, Ramón Martínez-cancino, Subhashini Sivagnanam, Kenneth Yoshimoto, Amitava Majumdar, Scott MakeigAbstract:The EEGLAB signal processing environment is a widely used open source software environment for processing electroencephalographic (EEG) data. The Neuroscience Gateway (nsgportal.org) is a software portal allowing users to readily run a variety of neuroimaging software on high performance computing (HPC) resources. We have expanded the current Neuroscience Gateway (NSG) services to enable researchers to freely run EEGLAB processing scripts and pipelines on their EEG or related data via the Neuroscience Gateway. This Open EEGLAB Portal is open to all for use in nonprofit projects and allows researchers to submit unimodal or multimodal EEG data for parallel processing using standard or custom EEGLAB processing pipelines. A detailed user tutorial is available (sccn.ucsd.edu/wiki/EEGLAB_on_NSG). As a proof of concept, we apply an EEGLAB pipeline to freely available 128-channel EEG data from 1,097 participants in the Child Mind Institute Healthy Brain Network project (childmind.org/center/healthy-brain-network).
-
NER - The Open EEGLAB portal
2019 9th International IEEE EMBS Conference on Neural Engineering (NER), 2019Co-Authors: Arnaud Delorme, Ramón Martínez-cancino, Subhashini Sivagnanam, Kenneth Yoshimoto, Amitava Majumdar, Scott MakeigAbstract:The EEGLAB signal processing environment is a widely used open source software environment for processing electroencephalographic (EEG) data. The Neuroscience Gateway (nsgportal.org) is a software portal allowing users to readily run a variety of neuroimaging software on high performance computing (HPC) resources. We have expanded the current Neuroscience Gateway (NSG) services to enable researchers to freely run EEGLAB processing scripts and pipelines on their EEG or related data via the Neuroscience Gateway. This Open EEGLAB Portal is open to all for use in nonprofit projects and allows researchers to submit unimodal or multimodal EEG data for parallel processing using standard or custom EEGLAB processing pipelines. A detailed user tutorial is available (sccn.ucsd.edu/wiki/EEGLAB_on_NSG). As a proof of concept, we apply an EEGLAB pipeline to freely available 128-channel EEG data from 1,097 participants in the Child Mind Institute Healthy Brain Network project (childmind.org/center/healthy-brain-network).
-
Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG
Frontiers in neuroinformatics, 2016Co-Authors: Nima Bigdely-shamlo, Scott Makeig, Makoto Miyakoshi, Jeremy Cockfield, Thomas Rognon, Chris La Valle, Kay A. RobbinsAbstract:Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/EEGLAB), CTAGGER, is also available to speed the process of tagging existing studies.
-
EMBC - Non-parametric group-level statistics for source-resolved ERP analysis
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2015Co-Authors: Clement Lee, Arnaud Delorme, Makoto Miyakoshi, Gert Cauwenberghs, Scott MakeigAbstract:We have developed a new statistical framework for group-level event-related potential (ERP) analysis in EEGLAB. The framework calculates the variance of scalp channel signals accounted for by the activity of homogeneous clusters of sources found by independent component analysis (ICA). When ICA data decomposition is performed on each subject's data separately, functionally equivalent ICs can be grouped into EEGLAB clusters. Here, we report a new addition (statPvaf) to the EEGLAB plug-in std_envtopo to enable inferential statistics on main effects and interactions in event related potentials (ERPs) of independent component (IC) processes at the group level. We demonstrate the use of the updated plug-in on simulated and actual EEG data.
Arnaud Delorme - One of the best experts on this subject based on the ideXlab platform.
-
The open EEGLAB portal Interface: High-Performance computing with EEGLAB.
NeuroImage, 2020Co-Authors: Ramón Martínez-cancino, Arnaud Delorme, Dung Truong, Fiorenzo Artoni, Kenneth Kreutz-delgado, Subhashini Sivagnanam, Kenneth Yoshimoto, Amitava Majumdar, Scott MakeigAbstract:Abstract EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently ( Delorme et al., 2019 ) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.
-
The Open EEGLAB portal
2019Co-Authors: Arnaud Delorme, Ramón Martínez-cancino, Subhashini Sivagnanam, Kenneth Yoshimoto, Amitava Majumdar, Scott MakeigAbstract:The EEGLAB signal processing environment is a widely used open source software environment for processing electroencephalographic (EEG) data. The Neuroscience Gateway (nsgportal.org) is a software portal allowing users to readily run a variety of neuroimaging software on high performance computing (HPC) resources. We have expanded the current Neuroscience Gateway (NSG) services to enable researchers to freely run EEGLAB processing scripts and pipelines on their EEG or related data via the Neuroscience Gateway. This Open EEGLAB Portal is open to all for use in nonprofit projects and allows researchers to submit unimodal or multimodal EEG data for parallel processing using standard or custom EEGLAB processing pipelines. A detailed user tutorial is available (sccn.ucsd.edu/wiki/EEGLAB_on_NSG). As a proof of concept, we apply an EEGLAB pipeline to freely available 128-channel EEG data from 1,097 participants in the Child Mind Institute Healthy Brain Network project (childmind.org/center/healthy-brain-network).
-
NER - The Open EEGLAB portal
2019 9th International IEEE EMBS Conference on Neural Engineering (NER), 2019Co-Authors: Arnaud Delorme, Ramón Martínez-cancino, Subhashini Sivagnanam, Kenneth Yoshimoto, Amitava Majumdar, Scott MakeigAbstract:The EEGLAB signal processing environment is a widely used open source software environment for processing electroencephalographic (EEG) data. The Neuroscience Gateway (nsgportal.org) is a software portal allowing users to readily run a variety of neuroimaging software on high performance computing (HPC) resources. We have expanded the current Neuroscience Gateway (NSG) services to enable researchers to freely run EEGLAB processing scripts and pipelines on their EEG or related data via the Neuroscience Gateway. This Open EEGLAB Portal is open to all for use in nonprofit projects and allows researchers to submit unimodal or multimodal EEG data for parallel processing using standard or custom EEGLAB processing pipelines. A detailed user tutorial is available (sccn.ucsd.edu/wiki/EEGLAB_on_NSG). As a proof of concept, we apply an EEGLAB pipeline to freely available 128-channel EEG data from 1,097 participants in the Child Mind Institute Healthy Brain Network project (childmind.org/center/healthy-brain-network).
-
EMBC - Non-parametric group-level statistics for source-resolved ERP analysis
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2015Co-Authors: Clement Lee, Arnaud Delorme, Makoto Miyakoshi, Gert Cauwenberghs, Scott MakeigAbstract:We have developed a new statistical framework for group-level event-related potential (ERP) analysis in EEGLAB. The framework calculates the variance of scalp channel signals accounted for by the activity of homogeneous clusters of sources found by independent component analysis (ICA). When ICA data decomposition is performed on each subject's data separately, functionally equivalent ICs can be grouped into EEGLAB clusters. Here, we report a new addition (statPvaf) to the EEGLAB plug-in std_envtopo to enable inferential statistics on main effects and interactions in event related potentials (ERPs) of independent component (IC) processes at the group level. We demonstrate the use of the updated plug-in on simulated and actual EEG data.
-
EEGLAB - an Open Source Matlab Toolbox for Electrophysiological Research.
Biomedizinische Technik. Biomedical engineering, 2013Co-Authors: Clemens Brunner, Arnaud Delorme, Scott MakeigAbstract:EEGLAB is a widely used open-source MAT- LAB toolbox for analysis of electrophysiological data. Us- ing EEGLAB, users can import various data formats, pre- process data (filter, resample, average, epoch), visualize data (signal browser, event-related potentials, power spec- tra), perform independent component analysis (ICA), use various time/frequency analysis methods such as event- related spectral perturbation (ERSP) and inter-trial co- herence (ITC). The extensible plug-in architecture enables third parties to contribute additional functionality such as source localization, connectivity estimation or the design of online brain-computer interfaces.
Elmar Lang - One of the best experts on this subject based on the ideXlab platform.
-
EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition.
Journal of neuroscience methods, 2015Co-Authors: Karema Al-subari, Saad Al-baddai, Ana Maria Tomé, Markus Goldhacker, Rupert Faltermeier, Elmar LangAbstract:Background Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field. New method EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis. Results EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox. Comparison with existing methods EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal. Conclusions EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.
-
Subspace techniques to remove artifacts from EEG: A quantitative analysis
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2008Co-Authors: Ana Teixeira, Ana Maria Tomé, Elmar Lang, A. Martins Da SilvaAbstract:In this work we discuss and apply projective subspace techniques to both multichannel as well as single channel recordings. The single-channel approach is based on singular spectrum analysis(SSA) and the multichannel approach uses the extended infomax algorithm which is implemented in the open-source toolbox EEGLAB. Both approaches will be evaluated using artificial mixtures of a set of selected EEG signals. The latter were selected visually to contain as the dominant activity one of the characteristic bands of an electroencephalogram (EEG). The evaluation is performed both in the time and frequency domain by using correlation coefficients and coherence function, respectively.
Nima Bigdely-shamlo - One of the best experts on this subject based on the ideXlab platform.
-
Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG
Frontiers in neuroinformatics, 2016Co-Authors: Nima Bigdely-shamlo, Scott Makeig, Makoto Miyakoshi, Jeremy Cockfield, Thomas Rognon, Chris La Valle, Kay A. RobbinsAbstract:Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/EEGLAB), CTAGGER, is also available to speed the process of tagging existing studies.
-
Measure projection analysis: a probabilistic approach to EEG source comparison and multi-subject inference.
NeuroImage, 2013Co-Authors: Nima Bigdely-shamlo, Tim Mullen, Kenneth Kreutz-delgado, Scott MakeigAbstract:A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/EEGLAB), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution.
-
EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing
Computational intelligence and neuroscience, 2011Co-Authors: Arnaud Delorme, Tim Mullen, Christian Kothe, Zeynep Akalin Acar, Nima Bigdely-shamlo, Andrey Vankov, Scott MakeigAbstract:We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for ComputationalNeuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments.
-
Brain-Computer Interfaces - MATLAB-Based Tools for BCI Research
Brain-Computer Interfaces, 2010Co-Authors: Arnaud Delorme, Christian Kothe, Nima Bigdely-shamlo, Andrey Vankov, Robert Oostenveld, Thorsten O. Zander, Scott MakeigAbstract:We first discuss two MATLAB-centered solutions for real-time data streaming, the environments FieldTrip (Donders Institute, Nijmegen) and DataSuite (Data- River, Producer, MatRiver) (Swartz Center, La Jolla). We illustrate the relative simplicity of coding BCI feature extraction and classification under MATLAB (The Mathworks, Inc.) using a minimalist BCI example, and then describe BCILAB (Team PhyPa, Berlin), a new BCI package that uses the data structures and extends the capabilities of the widely used EEGLAB signal processing environment. We finally review the range of standalone and MATLAB-based software currently freely available to BCI researchers.
Karema Al-subari - One of the best experts on this subject based on the ideXlab platform.
-
EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition.
Journal of neuroscience methods, 2015Co-Authors: Karema Al-subari, Saad Al-baddai, Ana Maria Tomé, Markus Goldhacker, Rupert Faltermeier, Elmar LangAbstract:Background Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field. New method EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis. Results EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox. Comparison with existing methods EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal. Conclusions EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.