Machine Learning

Ensemble Classifier for Eye State Classification using EEG Signals

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  • arXiv
    5 pts

    Ensemble Classifier for Eye State Classification using EEG Signals

    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.

    Ensemble Classifier for Eye State Classification using EEG Signals
    by Ali Al-Taei
    https://arxiv.org/pdf/1709.08590v2.pdf

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