Comparing the Performances of Ensemble-classifiers to Detect Eye State

Brain signals required for the brain-computer interface are obtained through the electroencephalography (EEG) method. EEG data is used in the analysis of many problems such as epileptic seizure detection, bipolar mood disorder, attention deficit, and detection of the sleep state of the vehicle driver. It is very important to determine whether the eye is open or closed, which is a substantial organ for the determination of the cognitive state of the person. The aim of this paper is to present a stable and successful model for detecting the eye states that are opened or closed. In this context, the performances of several ensemble classifiers were examined on the Emotiv EEG Neuroheadset dataset, which has 14 features excluding the target variable, 14980 records that have 8225 eye states opened and 6755 eye states closed. In the experiments, firstly the min-max normalization process was applied to the dataset, and then the classification performances of these classifiers were evaluated via a 5-fold cross-validation technique. The performance of each model was measured using accuracy, sensitivity, and specificity metrics. The obtained results show that the Random Forest algorithm is an acceptable level with 92.61% value of accuracy, 94.31% value of sensitivity and 91.36% value of specificity for detecting the eye state.