| Name that tune: Decoding music from the listening brain. | |
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MedLine Citation:
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PMID: 20541612 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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In the current study we use electroencephalography (EEG) to detect heard music from the brain signal, hypothesizing that the time structure in music makes it especially suitable for decoding perception from EEG signals. While excluding music with vocals, we classified the perception of seven different musical fragments of about three seconds, both individually and cross-participants, using only time domain information (the event-related potential, ERP). The best individual results are 70% correct in a seven-class problem while using single trials, and when using multiple trials we achieve 100% correct after six presentations of the stimulus. When classifying across participants, a maximum rate of 53% was reached, supporting a general representation of each musical fragment over participants. While for some music stimuli the amplitude envelope correlated well with the ERP, this was not true for all stimuli. Aspects of the stimulus that may contribute to the differences between the EEG responses to the pieces of music are discussed. |
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Authors:
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Rebecca S Schaefer; Jason Farquhar; Yvonne Blokland; Makiko Sadakata; Peter Desain |
Publication Detail:
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Type: Journal Article Date: 2010-06-10 |
Journal Detail:
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Title: NeuroImage Volume: 56 ISSN: 1095-9572 ISO Abbreviation: Neuroimage Publication Date: 2011 May |
Date Detail:
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Created Date: 2011-04-25 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9215515 Medline TA: Neuroimage Country: United States |
Other Details:
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Languages: eng Pagination: 843-9 Citation Subset: IM |
Copyright Information:
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Copyright © 2010 Elsevier Inc. All rights reserved. |
Affiliation:
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Donders Institute for Brain, Cognition and Behavior: Centre for Cognition, Montessorilaan 3, 6525 HE Radboud University Nijmegen, The Netherlands. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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