REMOVAL OF ARTIFACTS IN EYE BLINKS FROM EEG SIGNAL USING RECURRENT NEURAL NETWORKS

In this article, we delete the blink objects in all EEG signal channels obtained from WESN with their only restriction being the bandwidth. The research uses a clustered solution, Recurrent Neural Network with the use of spatio-temporal structure in different modules with the restriction of resources and extreme bandwidth in WESN. The objective of this analysis is to minimize energy consumption by removing blink artifacts correctly from the EEG signals and reducing the noise level of the EEG signals.