FEASIBILITY ANALYSIS OF ELECTROENCEPHALOGRAPHY MODEL FOR BRAIN TRAUMATIC SIGNAL ANALYSIS WITH DEEP LEARNING MODULE FOR ROBOTIC SURGERY ASSISTANCE

Abstract

The electrical process of a brain (EEG) shows vital intricate patterns by solid nonlinear as well as potent characteristics. The interaction in the brain cells comes about simply by electric impulses. It is assessed by means of positioning the electrodes upon the scalp of
the subject. The cortical nerve system cell inhibitory as well as excitatory postsynaptic possibilities create the EEG signs. In the event of trauma, that becomes challenging to acquire the valuable data via such impulses straight in the time domain merely by understanding them. Consequently, in the case of robotic surgical attempts, the deep learning module can be used to record and classify EEG signals. This paper presents the EEG functional overview which can be utilized for the development of a new EEG model.