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A brain computer interface (BCI) enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction and classification operations. Feature extraction is crucial as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery based 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 this study, commonly used fractal dimension estimation methods to characterize time series (Katz’s method, Higuchi’s method and the rescaled range method) were evaluated for feature extraction in motor imagery based BCI by conducting offline analyses of a two class motor imagery …