The electroencephalographic signals (EEG) are corrupted by various artifacts, among which the ocular ones prevail thus making their elimination necessary. The aim of this research, other than implementing an adaptive filtering and quantitatively comparing it to the method of linear regression, consisted of leaving a basis for considerations of its application in the reduction of ocular artifacts in EEG coming from real sources and delivering valuable results for those who work with EEG systems like, for instance, the followers of BCI (Brain Computer Interfaces).
This research was conducteded under the research ” Clasificación de señales EEG mediante redes neuronales [ Dr. Jaime Delgado Saa ] ” at the “Universidad del Norte“.
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