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Scale-Free Brain-Wave Music from Simultaneously EEG and fMRI Recordings

Overview of attention for article published in PLOS ONE, November 2012
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Title
Scale-Free Brain-Wave Music from Simultaneously EEG and fMRI Recordings
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0049773
Pubmed ID
Authors

Jing Lu, Dan Wu, Hua Yang, Cheng Luo, Chaoyi Li, Dezhong Yao

Abstract

In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Italy 3 2%
Spain 2 1%
Japan 2 1%
Austria 1 <1%
Canada 1 <1%
Finland 1 <1%
Chile 1 <1%
United Kingdom 1 <1%
Other 0 0%
Unknown 119 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 23%
Researcher 23 17%
Student > Master 18 13%
Student > Bachelor 14 10%
Professor > Associate Professor 8 6%
Other 22 16%
Unknown 18 13%
Readers by discipline Count As %
Engineering 26 19%
Psychology 19 14%
Neuroscience 18 13%
Computer Science 10 7%
Agricultural and Biological Sciences 9 7%
Other 29 22%
Unknown 23 17%