Eye state prediction using ensembled machine learning models

As electric signals are transmitted between the brain cell s for transferring of 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. We can also capture epileptic seizures that are caused by a disruption in the working of brain, by the Electro Encephalogram Test. 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 this work, 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. 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 ensembling of models will be done on best three predictive models pertaining to our dataset.