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Classification of SSVEP-based BCIs using Genetic Algorithm
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DOI：10.1186/s40537-021-00478-y.png)
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Abstract
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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.
Keywords:
Brain signal
Visual evoked signal
Classification
Genetic algorithm
Wavelet transform
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