Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data

Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed an...

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Published inBrain topography Vol. 31; no. 1; pp. 47 - 61
Main Authors Bridwell, David A., Rachakonda, Srinivas, Silva, Rogers F., Pearlson, Godfrey D., Calhoun, Vince D.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.01.2018
Springer Nature B.V
Subjects
Adult
Aged
Algorithms
Biomedical and Life Sciences
Biomedicine
Blindness
Brain Mapping
Cognition - physiology
Cognitive ability
Computer programs
Computer Simulation
Decomposition
EEG
Electroencephalography
Electroencephalography - methods
Electroencephalography - statistics & numerical data
Feasibility Studies
Humans
Male
Middle Aged
Models, Neurological
Nerve Net - anatomy & histology
Nerve Net - physiology
Neurology
Neurosciences
Original Paper
Oscillations
Psychiatry
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Software
Wavelet Analysis
Young Adult
Simulated EEG
Wavelets
Multi-subject decomposition
Blind source separation
Resting EEG
ICA
Online AccessGet full text

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Abstract Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.
AbstractList Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.
Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 second (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http:/mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.
Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.
Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.
Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox (http://mialab.mrn.org/software/eegift/) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.
Author Rachakonda, Srinivas
Bridwell, David A.
Pearlson, Godfrey D.
Calhoun, Vince D.
Silva, Rogers F.
AuthorAffiliation c Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA
e Olin Neuropsychiatry Research Center, Hartford Healthcare Corporation, Hartford, CT 06106, USA
b Department of ECE, University of New Mexico, Albuquerque, NM 87131, USA
a The Mind Research Network, Albuquerque, NM 87131, USA
d Department of Neurobiology, Yale University School of Medicine, New Haven, CT 06510, USA
AuthorAffiliation_xml – name: a The Mind Research Network, Albuquerque, NM 87131, USA
– name: e Olin Neuropsychiatry Research Center, Hartford Healthcare Corporation, Hartford, CT 06106, USA
– name: c Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA
– name: b Department of ECE, University of New Mexico, Albuquerque, NM 87131, USA
– name: d Department of Neurobiology, Yale University School of Medicine, New Haven, CT 06510, USA
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  orcidid: 0000-0002-4119-9795
  surname: Bridwell
  fullname: Bridwell, David A.
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  organization: The Mind Research Network
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  givenname: Srinivas
  surname: Rachakonda
  fullname: Rachakonda, Srinivas
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  givenname: Rogers F.
  surname: Silva
  fullname: Silva, Rogers F.
  organization: The Mind Research Network, Department of ECE, University of New Mexico
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  givenname: Godfrey D.
  surname: Pearlson
  fullname: Pearlson, Godfrey D.
  organization: Department of Psychiatry, Yale University School of Medicine, Department of Neurobiology, Yale University School of Medicine, Olin Neuropsychiatry Research Center, Hartford Healthcare Corporation
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  surname: Calhoun
  fullname: Calhoun, Vince D.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/26909688$$D View this record in MEDLINE/PubMed
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IEDL.DBID U2A
ISSN 0896-0267
1573-6792
IngestDate Thu Aug 21 18:16:55 EDT 2025
Tue Aug 05 09:49:40 EDT 2025
Sat Aug 30 19:11:00 EDT 2025
Thu Apr 03 06:49:01 EDT 2025
Tue Jul 01 02:16:34 EDT 2025
Thu Apr 24 22:56:25 EDT 2025
Fri Feb 21 02:32:24 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords Simulated EEG
Wavelets
Multi-subject decomposition
Blind source separation
Resting EEG
ICA
Language English
LinkModel DirectLink
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crossref_citationtrail_10_1007_s10548_016_0479_1
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PublicationSubtitle A Journal of Cerebral Function and Dynamics
PublicationTitle Brain topography
PublicationTitleAbbrev Brain Topogr
PublicationTitleAlternate Brain Topogr
PublicationYear 2018
Publisher Springer US
Springer Nature B.V
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Snippet Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency...
Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency...
Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 second (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct...
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StartPage 47
SubjectTerms Adult
Aged
Algorithms
Biomedical and Life Sciences
Biomedicine
Blindness
Brain Mapping
Cognition - physiology
Cognitive ability
Computer programs
Computer Simulation
Decomposition
EEG
Electroencephalography
Electroencephalography - methods
Electroencephalography - statistics & numerical data
Feasibility Studies
Humans
Male
Middle Aged
Models, Neurological
Nerve Net - anatomy & histology
Nerve Net - physiology
Neurology
Neurosciences
Original Paper
Oscillations
Psychiatry
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Software
Wavelet Analysis
Young Adult
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Title Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data
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Volume 31
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