Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure etc. With latest advances deep learning (DL) models, it is possible design accurate and prompt EEG EyeState problem. In this view, study presents a novel compact bat algorithm with model for biomedical (CBADL-BEESC) model. The major intention CBADL-BEESC technique aims categorize presence EyeState. performs feature extraction using ALexNet which helps produce useful vectors. addition, extreme machine autoencoder (ELM-AE) applied classify signals parameter tuning ELM-AE performed CBA. experimental result analysis carried out on benchmark results comparative outcome reported supremacy over recent methods.