Scale-free music of the brain
There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and mos...
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| Veröffentlicht in: | PloS one 2009-06, Vol.4 (6), p.e5915-e5915 |
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| description | There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG.
In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG) waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM) and slow-wave sleep (SWS). The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN), 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(kappa = 0.800, P |
| doi_str_mv | 10.1371/journal.pone.0005915 |
| format | Article |
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In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG) waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM) and slow-wave sleep (SWS). The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN), 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(kappa = 0.800, P<0.001). We also applied the method to the EEG data from eyes closed, eyes open and epileptic EEG, and the results showed these mental states can be identified by listeners.
The sonification rules may identify the mental states of the brain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0005915</identifier><identifier>PMID: 19526057</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acoustics ; Adolescent ; Adult ; Algorithms ; Brain ; Brain - physiology ; Brain Mapping ; Computer music ; Education ; EEG ; Electroencephalography ; Electroencephalography - methods ; Epilepsy ; Epilepsy - physiopathology ; Eye ; Eye movements ; Feedback ; Female ; Frequency ; Humans ; Laboratories ; Life sciences ; Male ; Mapping ; Mathematics/Fractals ; Mathematics/Nonlinear Dynamics ; Middle Ages ; Music ; Musical instruments ; Neuroscience ; Physiology ; REM sleep ; Reproducibility of Results ; Sleep ; Sleep Stages ; Sleep, REM ; Sound ; Wakefulness ; Waveforms ; White noise ; Zipf's Law</subject><ispartof>PloS one, 2009-06, Vol.4 (6), p.e5915-e5915</ispartof><rights>COPYRIGHT 2009 Public Library of Science</rights><rights>2009 Wu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Wu et al. 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c662t-71f913bcc10e511b4b478bd42b5dedfba9794139becb3e5d78a39572419559be3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2691588/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2691588/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19526057$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Scalas, Enrico</contributor><creatorcontrib>Wu, Dan</creatorcontrib><creatorcontrib>Li, Chao-Yi</creatorcontrib><creatorcontrib>Yao, De-Zhong</creatorcontrib><title>Scale-free music of the brain</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG.
In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG) waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM) and slow-wave sleep (SWS). The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN), 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(kappa = 0.800, P<0.001). We also applied the method to the EEG data from eyes closed, eyes open and epileptic EEG, and the results showed these mental states can be identified by listeners.
The sonification rules may identify the mental states of the brain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy.</description><subject>Acoustics</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Algorithms</subject><subject>Brain</subject><subject>Brain - physiology</subject><subject>Brain Mapping</subject><subject>Computer music</subject><subject>Education</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>Epilepsy</subject><subject>Epilepsy - physiopathology</subject><subject>Eye</subject><subject>Eye movements</subject><subject>Feedback</subject><subject>Female</subject><subject>Frequency</subject><subject>Humans</subject><subject>Laboratories</subject><subject>Life sciences</subject><subject>Male</subject><subject>Mapping</subject><subject>Mathematics/Fractals</subject><subject>Mathematics/Nonlinear Dynamics</subject><subject>Middle Ages</subject><subject>Music</subject><subject>Musical instruments</subject><subject>Neuroscience</subject><subject>Physiology</subject><subject>REM sleep</subject><subject>Reproducibility of Results</subject><subject>Sleep</subject><subject>Sleep Stages</subject><subject>Sleep, REM</subject><subject>Sound</subject><subject>Wakefulness</subject><subject>Waveforms</subject><subject>White noise</subject><subject>Zipf's Law</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNktuKFDEQhhtR3HX1DTwMCAtezJh0OqcbYVk8DCwsuOptSNKVmQzpzmzSLfr2Zp1Wp8ULyUVC5au_UpW_qp5itMKE49e7OKZeh9U-9rBCCFGJ6b3qFEtSL1mNyP2j80n1KOddYYhg7GF1giWtGaL8tHp2Y3WApUsAi27M3i6iWwxbWJikff-4euB0yPBk2s-qz-_efrr8sLy6fr--vLhaWsbqYcmxk5gYazECirFpTMOFaZva0BZaZ7TkssFEGrCGAG250ERSXjflHbREyVn14qC7DzGrqbOscC0kFoJJXoj1gWij3ql98p1O31XUXv0MxLRROg3eBlAONAeHaq6tbjiXkoG2sgbhuATT2KL1Zqo2mg5aC_2QdJiJzm96v1Wb-FXVrMxYiCJwPgmkeDtCHlTns4UQdA9xzIpxwhtCWAFf_gX-u7fVgdqUr1C-d7FUtWW10Hlbvtf5Er9oOEFMUElLwqtZQmEG-DZs9JizWt98_H_2-sucPT9it6DDsM0xjIOPfZ6DzQG0KeacwP0eHkbqzp2_-lR37lSTO0va8-PB_0ma7Eh-AEpk3ro</recordid><startdate>20090615</startdate><enddate>20090615</enddate><creator>Wu, Dan</creator><creator>Li, Chao-Yi</creator><creator>Yao, De-Zhong</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20090615</creationdate><title>Scale-free music of the brain</title><author>Wu, Dan ; Li, Chao-Yi ; Yao, De-Zhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c662t-71f913bcc10e511b4b478bd42b5dedfba9794139becb3e5d78a39572419559be3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Acoustics</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Algorithms</topic><topic>Brain</topic><topic>Brain - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Dan</au><au>Li, Chao-Yi</au><au>Yao, De-Zhong</au><au>Scalas, Enrico</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Scale-free music of the brain</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2009-06-15</date><risdate>2009</risdate><volume>4</volume><issue>6</issue><spage>e5915</spage><epage>e5915</epage><pages>e5915-e5915</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG.
In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG) waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM) and slow-wave sleep (SWS). The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN), 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(kappa = 0.800, P<0.001). We also applied the method to the EEG data from eyes closed, eyes open and epileptic EEG, and the results showed these mental states can be identified by listeners.
The sonification rules may identify the mental states of the brain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>19526057</pmid><doi>10.1371/journal.pone.0005915</doi><tpages>e5915</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Acoustics Adolescent Adult Algorithms Brain Brain - physiology Brain Mapping Computer music Education EEG Electroencephalography Electroencephalography - methods Epilepsy Epilepsy - physiopathology Eye Eye movements Feedback Female Frequency Humans Laboratories Life sciences Male Mapping Mathematics/Fractals Mathematics/Nonlinear Dynamics Middle Ages Music Musical instruments Neuroscience Physiology REM sleep Reproducibility of Results Sleep Sleep Stages Sleep, REM Sound Wakefulness Waveforms White noise Zipf's Law |
| title | Scale-free music of the brain |
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