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- W2760752793 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." @default.
- W2760752793 created "2017-10-06" @default.
- W2760752793 creator A5047380202 @default.
- W2760752793 date "2017-09-25" @default.
- W2760752793 modified "2023-09-27" @default.
- W2760752793 title "Ensemble Classifier for Eye State Classification using EEG Signals" @default.
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