| dc.creator | Bueno-López, Maximiliano | |
| dc.creator | Muñoz-Gutiérrez, Pablo A. | |
| dc.creator | Giraldo, Eduardo | |
| dc.creator | Molinas, Marta | |
| dc.date | 2018-01-01T08:00:00Z | |
| dc.date.accessioned | 2022-10-13T13:36:17Z | |
| dc.date.available | 2022-10-13T13:36:17Z | |
| dc.identifier | https://ciencia.lasalle.edu.co/scopus_unisalle/269 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4157675 | |
| dc.description | The applications of Empirical Mode Decomposition (EMD) in Biomedical Signal analysis have increased and is common now to find publications that use EMD to identify behaviors in the brain or heart. EMD has shown excellent results in the identification of behaviours from the use of electroencephalogram (EEG) signals. In addition, some advances in the computer area have made it possible to improve their performance. In this paper, we presented a method that, using an entropy analysis, can automatically choose the relevant Intrinsic Mode Functions (IMFs) from EEG signals. The idea is to choose the minimum number of IMFs to reconstruct the brain activity. The EEG signals were processed by EMD and the IMFs were ordered according to the entropy cost function. The IMFs with more relevant information are selected for the brain mapping. To validate the results, a relative error measure was used. | |
| dc.source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
| dc.source | 319 | |
| dc.subject | Brain mapping | |
| dc.subject | Empirical mode decomposition | |
| dc.subject | Epilepsy | |
| dc.subject | Signal analysis | |
| dc.title | Analysis of epileptic activity based on brain mapping of EEG adaptive time-frequency decomposition | |
| dc.type | Conference Proceeding | |
