Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery‐Based Brain Computer Interface

Abstract

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. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method.

Feature Extraction of EEG Signal upon BCI Systems Based on Steady-State Visual Evoked Potentials Using the Ant Colony Optimization Algorithm

“…A brain computer interface (BCI) enables direct communication between a brain and a computer translating brain activity into computer commands, thus providing no muscular interaction with the environment [1]. The development of brain computer interface (BCI) systems is mainly based on the information obtained by processing brain signals obtained by electroencephalogram (EEG).…”

Comparativo de los Algoritmos de Dimensión Fractal Higuchi, Katz y Multiresolución de Conteo de Cajas en Señales EEG Basadas en Potenciales Relacionados por Eventos

“…73-83 (Harne, 2014), (Katz, 1988), (Khoa y Toi, 2012), (Loo et al, 2011), (Paramanathan y Uthayakumar, 2008), (Polychronaki et al, 2010), (Raghavendra y Dutt, 2009) (Gálvez et al, 2013), (Martins et al, 2012), (Millán et al, 2014), (Perlingeiro et al, 2005). El presente documento se centra en señales experimentales derivados de EEG y los algoritmos propuestos son los algoritmos de Higuchi, Katz, y el método de Multi-resolución de Conteo de Cajas (MRCC); sus resultados son ampliamente aplicables a cualquier tipo de señal.…”

“…); Loo et al (2011) utilizó señales EEG basadas en imágenes motoras para sistemas BCI; Bashashati et al (2003) se basó en métodos de FD para identificar los componentes de control de las señales de EEG en sistemas BCI; Esteller et al (2001) y Raghavendra y Dutt (2010) utilizaron seña-les sintéticas como series de datos para el cálculo de FD basándose en el comportamiento fractal similar al de las señales de EEG.…”

Dimension Estimation Using Weighted Correlation Dimension Method

“…Many engineering applications are difficult to be analyzed by traditional methods owing to the existence of high dimensional signals, such as face recognition [1][2][3], nonlinear dynamic systems [4,5], and fault diagnosis. Therefore, a qualified dimension reduction for the high dimension signals is necessary before further proceeding.…”