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The growing importance and utilization of measuring brain waves (e.g. EEGsignals of eye state) in brain-computer interface (BCI) applicationshighlighted the need for suitable classification methods. In this paper, acomparison between three of well-known classification methods (i.e. supportvector machine (SVM), hidden Markov map (HMM), and radial basis function (RBF))for EEG based eye state classification was achieved. Furthermore, a suggestedmethod that is based on ensemble model was tested. The suggested (ensemblesystem) method based on a voting algorithm with two kernels: random forest (RF)and Kstar classification methods. The performance was tested using threemeasurement parameters: accuracy, mean absolute error (MAE), and confusionmatrix. Results showed that the proposed method outperforms the other testedmethods. For instance, the suggested method's performance was 97.27% accuracyand 0.13 MAE.

Ensemble Classifier for Eye State Classification using EEG Signals