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dc.contributor.author | Córdova, Felisa | |
dc.contributor.author | Hernán Díaz, M. | |
dc.date.accessioned | 2024-09-26T00:46:53Z | |
dc.date.available | 2024-09-26T00:46:53Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 1877-0509 | |
dc.identifier.uri | https://repositorio.uss.cl/handle/uss/13507 | |
dc.description | Funding Information: This work was supported by the Faculty of Engineering at University San Sebastian, Chile and the Department of Mathematics and Computer Science at the Faculty of Science, University of Santiago de Chile. Publisher Copyright: © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative Management. | |
dc.description.abstract | A method for detecting and monitoring the long-term time course of brain processes is described for two different conditions: i) A abbreviated version of a visual problem-solving IQ (Intelligent Quotient) Raven test and ii) A perceptual video task consisting of watching a sequence of (3 minutes each) 5 movie genera: animation, war, romance, violence and thriller. The method consisted of the analysis of the grayscale pixel values of the spectrographic image, generated by a Fast Fourier Transform (FFT), applied over the amplitude v/s time domain of the electroencephalographic (EEG) signal recorded during the execution of the test/task. Results show a high-resolution tool for detecting and monitoring brain signals in very narrow frequency range (0,05 Hz) and time (100 ms). Subject-dependent individual differences (N=5) are evident and a number up to 10 frequency communication channels can be detected in narrow frequency range of 0,5 Hz. Combining both aspects of these tool, high resolution of the frequency range, and the subject-dependent individual differences found in the execution of the test/task, we plan to use this information to classify subjects according problem-solving and perceptual brain processing differences and learning preferences. | en |
dc.language.iso | eng | |
dc.relation.ispartof | vol. 221 Issue: Pages: 1402-1407 | |
dc.source | Procedia Computer Science | |
dc.title | Brain Communication : A system where to investigate and re-engineer human communication and learning processes | en |
dc.type | Artículo de conferencia | |
dc.identifier.doi | 10.1016/j.procs.2023.08.131 | |
dc.publisher.department | Facultad de Ingeniería y Tecnología | |
dc.publisher.department | Facultad de Ingeniería, Arquitectura y Diseño |
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