Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction

Dalila Trad, Tarik Al-Ani, Mohamed Jemni


The idea that brain activity could be used as a communication channel has rapidly developed. Indeed, Electroencephalography (EEG) is the most common technique to measure the brain activity on the scalp and in real-time. In this study we examine the use of EEG signals in Brain Computer Interface (BCI). This approach consists of combining the Empirical Mode Decomposition (EMD) and band power (BP) for the extraction of EEG signals in order to classify motor imagery (MI). This new feature extraction approach is intended for non-stationary and non-linear characteristics MI EEG. The EMD method is proposed to decompose the EEG signal into a set of stationary time series called Intrinsic Mode Functions (IMF). These IMFs are analyzed with the bandpower (BP) to detect the characteristics of sensorimotor rhythms (mu and beta) when a subject imagines a left or right hand movement. Finally, the data were just reconstructed with the specific IMFs and the bandpower is applied on the new database. Once the new feature vector is rebuilt, the classification of MI is performed using two types of classifiers: generative and discriminant. The results obtained show that the EMD allows the most reliable features to be extracted from EEG and that the classification rate obtained is higher and better than using the direct BP approach only. Such a system is a promising communication channel for people suffering from severe paralysis, for instance, people with myopathic diseases or muscular dystrophy (MD) in order to help them move a joystick to a desired direction corresponding to the specific motor imagery.


Brain Computer Interface, motor imagery, Bandpower, Empirical Mode Decomposition, Hidden Markov Model, Support Vector Machines, Cohens kappa coefficient

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