Abstract
In last few years, the research community has shown interest in the development of Brain Computer Interface which may assists physically challenged people to communicate with the help of brain signal. The two important components of such BCI system are to determine appropriate features and classification method to achieve better performance. In literature, Empirical Mode Decomposition is suggested for feature extraction from EEG which is suitable for the analysis of non-linear and non-stationary time series. However, the features obtained from EEG may contain irrelevant and redundant features which make them inefficient for machine learning. Relevant features not only decrease the processing time to train a classifier but also provide better generalization. Hence, relevant features which provide maximum classification accuracy are selected using ratio of scatter matrices, Chernoff distance measure and linear regression. The performance of different mental task using different measures used for feature selection is compared and evaluated in terms of classification accuracy. Experimental results show that there is significant improvement in classification accuracy with features selected using all feature selection methods and in particular with ratio of scatter matrices.
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Gupta, A., Agrawal, R.K. (2012). Relevant Feature Selection from EEG Signal for Mental Task Classification. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30220-6_36
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