Semi-automatic identification of independent components representing EEG artifact
Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed...
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| Published in | Clinical neurophysiology Vol. 120; no. 5; pp. 868 - 877 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Ireland Ltd
01.05.2009
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1388-2457 1872-8952 |
| DOI | 10.1016/j.clinph.2009.01.015 |
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| Abstract | Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings.
CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA.
For eye-related artifacts, a very high degree of overlap between users (phi
>
0.80), and between users and CORRMAP (phi
>
0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi
<
0.70), and between users and CORRMAP (phi
<
0.65).
These results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components.
CORRMAP helps to efficiently use ICA for the removal EEG artifacts. |
|---|---|
| AbstractList | Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings.
CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA.
For eye-related artifacts, a very high degree of overlap between users (phi
>
0.80), and between users and CORRMAP (phi
>
0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi
<
0.70), and between users and CORRMAP (phi
<
0.65).
These results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components.
CORRMAP helps to efficiently use ICA for the removal EEG artifacts. Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. For eye-related artifacts, a very high degree of overlap between users (phi>0.80), and between users and CORRMAP (phi>0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi<0.70), and between users and CORRMAP (phi<0.65). These results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components. CORRMAP helps to efficiently use ICA for the removal EEG artifacts. Objective - Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. Methods - CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. Results - For eye-related artifacts, a very high degree of overlap between users (phi > 0.80), and between users and CORRMAP (phi > 0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi < 0.70), and between users and CORRMAP (phi < 0.65). Conclusions - These results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components. Significance - CORRMAP helps to efficiently use ICA for the removal EEG artifacts. Abstract Objective Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. Methods CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. Results For eye-related artifacts, a very high degree of overlap between users (phi > 0.80), and between users and CORRMAP (phi > 0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi < 0.70), and between users and CORRMAP (phi < 0.65). Conclusions These results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components. Significance CORRMAP helps to efficiently use ICA for the removal EEG artifacts. OBJECTIVEIndependent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings.METHODSCORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA.RESULTSFor eye-related artifacts, a very high degree of overlap between users (phi>0.80), and between users and CORRMAP (phi>0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi<0.70), and between users and CORRMAP (phi<0.65).CONCLUSIONSThese results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components.SIGNIFICANCECORRMAP helps to efficiently use ICA for the removal EEG artifacts. |
| Author | Edmonds, Barrie Schneider, Till Debener, Stefan Campos Viola, Filipa Eichele, Tom Thorne, Jeremy |
| Author_xml | – sequence: 1 givenname: Filipa surname: Campos Viola fullname: Campos Viola, Filipa organization: MRC Institute of Hearing Research, Southampton, UK – sequence: 2 givenname: Jeremy surname: Thorne fullname: Thorne, Jeremy organization: MRC Institute of Hearing Research, Southampton, UK – sequence: 3 givenname: Barrie surname: Edmonds fullname: Edmonds, Barrie organization: MRC Institute of Hearing Research, Nottingham, UK – sequence: 4 givenname: Till surname: Schneider fullname: Schneider, Till organization: Department of Neurophysiology and Pathophysiology, Center of Experimental Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany – sequence: 5 givenname: Tom surname: Eichele fullname: Eichele, Tom organization: Department of Biological and Medical Psychology, University of Bergen, Norway – sequence: 6 givenname: Stefan surname: Debener fullname: Debener, Stefan email: stefan@debener.de organization: MRC Institute of Hearing Research, Southampton, UK |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21543298$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/19345611$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | 2009 International Federation of Clinical Neurophysiology International Federation of Clinical Neurophysiology 2009 INIST-CNRS |
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| Keywords | Artifact correction Lateral eye movements ICA EEG Eye blinks Independent component analysis Human User Decision making Objective analysis Central nervous system Electrophysiology Artefact Electroencephalography Interpretation Eye movement Encephalon Eye Visual system Electrodiagnosis Performance |
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| Snippet | Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals.... Abstract Objective Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and... Objective - Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related... OBJECTIVEIndependent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related... |
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| SubjectTerms | Algorithms Artifact correction Artificial Intelligence Biological and medical sciences Brain - physiology Brain Mapping - methods Computer Simulation EEG Electrodiagnosis. Electric activity recording Electroencephalography - methods Evoked Potentials - physiology Eye and associated structures. Visual pathways and centers. Vision Eye blinks Eye Movements - physiology Fundamental and applied biological sciences. Psychology Heart Rate - physiology Humans ICA Independent component analysis Investigative techniques, diagnostic techniques (general aspects) Lateral eye movements Medical sciences Nervous system Neurology Pattern Recognition, Automated - methods Signal Processing, Computer-Assisted Software Software Validation Vertebrates: nervous system and sense organs |
| Title | Semi-automatic identification of independent components representing EEG artifact |
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