Eye State Prediction using Ensembled Machine Learning Moels

Abstract

As electric signals are transmitted between the brain cells for transferring data within the brain, capturing of these signals can result in understanding the functionality of brain and other directly linked parts (like eyes, ears, spinal nerves etc) of our body. It is done by Electro Encephalogram Test (EEG). Along with capturing Normal electric signals we can also capture epileptic seizures which are caused due to disruption in the normal working of brain. These electric signals are to be captured by small electrodes placed on human scalp using a standard 10/20 system on an Electro Encephalograph monitor in form of waves. These wave forms are transmitted to form of data for getting required information from data collected. In this dissertation, we will predict the state of eye (open or closed) by exploring 13 machine learning models on a 15 features dataset of an EEG test. The records of 14 electrodes are used for this prediction. Machine learning models in R language are statistical analysis and prediction analysis methods used on dataset by training and testing of the dataset. Results are evaluated using 6 different machine learning parameters i.e. Sensitivity, Confusion matrix, Kappa value, Specificity, Accuracy and Receiver Operating Characteristics (ROC) curve. K- Fold validation and assembling of models will be done on best three predictive models pertaining to our dataset.