Abstract
Brain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.
Highlights
A brain-computer interface (BCI), called “a mind-machine interface”, is a bridge between the human brain and an external device
Materials and methods This paper proposes a model for feature extraction, feature selection and, classification
The support vector machine (SVM) classifier is very sensitive to the feature selection strategy [13]
Summary
A brain-computer interface (BCI), called “a mind-machine interface”, is a bridge between the human brain and an external device This bridge is a new communication pathway which is expanding. This interface consists of a set of sensors and signal processing components that directly convert one’s brain activity into a series of communication or control signals. In this system, brain waves should firstly be captured using a brain wave recording apparatus. Sözer et al [5] presented a combined approach for extracting features from SSVEP signals.
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