Eye State Classification Based on EEG Signals
Abstract
EEG (electroencephalogram) signals are used to study the information about the state of the brain. Such information is useful for monitoring mental state of an individual and can avoid critical situations in one’s life. In this paper, we have implemented various machine learning algorithms on EEG eye state dataset to predict human eye state based on previous EEG signals. Random forest, decision tree, FURIA and bagging were implemented for comparative analysis. The experiment shows that random forest obtained the prediction accuracy of 92.43% which is the best among other implemented techniques.