Title: Ensemble Classifier for Eye State Classification using EEG Signals,
Abstract: The growing importance and utilization of measuring brain waves (e.g. EEG
signals of eye state) in brain-computer interface (BCI) applications
highlighted the need for suitable classification methods. In this paper, a
comparison between three of well-known classification methods (i.e. support
vector machine (SVM), hidden Markov map (HMM), and radial basis function (RBF))
for EEG based eye state classification was achieved. Furthermore, a suggested
method that is based on ensemble model was tested. The suggested (ensemble
system) method based on a voting algorithm with two kernels: random forest (RF)
and Kstar classification methods. The performance was tested using three
measurement parameters: accuracy, mean absolute error (MAE), and confusion
matrix. Results showed that the proposed method outperforms the other tested
methods. For instance, the suggested method's performance was 97.27% accuracy
and 0.13 MAE.