Computational Considerations in Security of Electronic Commerce Systems (ECS)
|Author(s)||Abbas. Najafizadeh, Abdolreza Asadi Ghanbari|
|Issue Date||September, 2014|
|Publishing Date||September, 2014|
|Keywords||Feature extraction; Fractal dimension; Principal Component Analysis (PCA); Independent Component Analysis (ICA).|
Feature extraction is the process of accurately simplifying the representation of data by reducing its dimensionality while extracting its relevant characteristics for the desired task. It has a substantial effect on the classification accuracy and speed since classification carried out without a successful feature extraction process on a high dimensional and redundant data would be computationally complex and would overfit the training data. Fractal dimension is a statistical measure indicating the complexity of an object or a quantity that is self-similar over some region of space or time interval. It has been successfully used in various domains to characterize such objects and quantities but its usage in BCI has been more recent. There are several fractal dimension estimation methods, some of which are not applicable to all types of data exhibiting fractal properties. In order to achieve a higher classification accuracy and speed, the fractal dimension estimation method that is most suitable to the data at hand should be chosen.
In this study, after preprocess the EEG data by the coherence average, principal component analysis (PCA), and independent component analysis (ICA) commonly used fractal dimension estimation methods Katz's method, Higuchi's method, the rescaled range (R/S) method, were evaluated for feature extraction in EEG based BCI by conducting offline analyses of a two class EEG dataset. Support vector machine (SVM) and linear discriminant analysis (LDA) were tested in combination with these methods to determine the methodology with the best performance and result compare with wavelet feature extraction method.