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dc.contributor.authorVelasquez Martinez, Luisa F.-
dc.contributor.authorZapata Castaño, F. Y.-
dc.contributor.authorCárdenas Peña, David-
dc.contributor.authorCastellanos Dominguez, Germán-
dc.coverage.spatial7004624en_US
dc.date.accessioned2021-07-14T12:58:33Z-
dc.date.available2021-07-14T12:58:33Z-
dc.date.issued2018-
dc.identifier.citationVelasquez-Martinez L.F., Zapata-Castaño F.Y., Cárdenas-Peña D., Castellanos-Dominguez G. (2018) Detecting EEG Dynamic Changes Using Supervised Temporal Patterns. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_40en_US
dc.identifier.urihttps://repositorio.uci.cu/jspui/handle/123456789/9476-
dc.description.abstractThe electroencephalogram signal records the neural activation at electrodes placed over the scalp. Brain-Computer Interfaces decode brain activity measured by EEG to send commands to external devices. The most well-known BCI systems are based on Motor Imagery paradigm that corresponds to the imagination of a motor action without execution. Event-Related Desynchronization and Synchronization shows the channel-wise temporal dynamics related to the motor activity. However, ERD/S demands the application of a large bank of narrowband filters to find dynamic changes and the assumption of temporal alignment ignores the between-trial temporal variations of neuronal activity. Taking to account the temporal variations, this work introduces a signal filtering analysis based on the estimation of Supervised Temporal Patterns that decode brain dynamics in MI paradigm which result from the solution of a generalized eigenvalues problem. The signal filtering analysis detects temporal dynamics related to MI tasks within each trial. The method highlights MI activity along channels and trials and shows differences between subjects performing these kinds of tasks.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.subjectSUPERVISED TEMPORAL PATTERNSen_US
dc.subjectEEG SIGNALen_US
dc.subjectMOTOR IMAGERYen_US
dc.subjectTEMPORAL DYNAMICSen_US
dc.titleDetecting EEG Dynamic Changes Using Supervised Temporal Patternsen_US
dc.typeconferenceObjecten_US
dc.rights.holderUniversidad de las Ciencias Informáticasen_US
dc.identifier.doihttps://doi.org/10.1007/978-3-030-01132-1_40-
dc.source.initialpage351en_US
dc.source.endpage358en_US
dc.source.titleUCIENCIA 2018en_US
dc.source.conferencetitleUCIENCIAen_US
Aparece en las colecciones: UCIENCIA 2018

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