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dc.contributor.author Díaz M, Hernán A.
dc.contributor.author Córdova, Felisa
dc.contributor.author Ozimisa, Gina
dc.contributor.author Fuentes, Hernán Díaz
dc.date.accessioned 2024-09-26T00:33:01Z
dc.date.available 2024-09-26T00:33:01Z
dc.date.issued 2022
dc.identifier.issn 1877-0509
dc.identifier.uri https://repositorio.uss.cl/handle/uss/12565
dc.description Funding Information: The present study was conducted as part of the thesis research program supported by the Neuromathlab, in the Department of Mathematics and Computer Science, Faculty of Science, University of Santiago de Chile. Publisher Copyright: © 2022 The Authors. Published by Elsevier B.V.
dc.description.abstract In this paper we report the use of a transformation of an EEG signal to a MIDI music representation. The subsequent analysis of the melodic and harmonic structure of this musical representation generated from the EEG, allowed us to have an image of the differential use of communication channels used by the brain, and that can be characterized by its frequency harmonic resonance pattern. The musical model has been previously proved to be very useful and informative with respect to some hidden functional structures, hard to detect from observing data in a table, a set of points or a bar plot describing the phenomena. Here we combined the possibility to have access to the audible experience on the EEG and the visual tools to represent this multidimensional experience, in a 2D mapping depiction. EEG data coming from 11 subjects were transformed into music, to use the two frontal electrodes (AF3 and AF4) and build a stereo musical piece, constructed with the left and right EEG signals coming from the frontal areas of the brain cortex. Results showed high intra- and inter-individual differences, when comparing the predominant frequency resonant structures. We called "mindchords"to this resonant frequency patterns, because we use a musical chords representation for detect and label specific patterns of brain functional dynamic, described in this way. The tool allows an easy characterization of the predominant resonant structures that populates the brain of the sample subjects, during basal, closed eyes, resting condition. en
dc.language.iso eng
dc.relation.ispartof vol. 214 Issue: no. C Pages: 720-726
dc.source Procedia Computer Science
dc.title Mindchords : A way to identify people's brains functional dynamics through a musical representation of the EEG en
dc.type Artículo de conferencia
dc.identifier.doi 10.1016/j.procs.2022.11.234
dc.publisher.department Facultad de Ingeniería y Tecnología
dc.publisher.department Facultad de Ingeniería, Arquitectura y Diseño


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