A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG.
Journal:
Journal of neural engineering
Published Date:
Oct 15, 2020
Abstract
OBJECTIVE: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance.