Empirical mode decomposition of EEG signals for brain computer interface

Motor imagery (MI) based brain-computer interface (BCI) systems show potential applications in neural rehabilitation. In MI-BCI systems, the brain signals from movement imagination, without actual movement of limbs, can be acquired, processed and characterized to translate into actionable signals that can be used to activate external devices. However, success of such MI-BCI systems, depends on the reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of characteristic features for effective classification of MI activity and translation into corresponding actions. In this work, a signal processing technique, namely, empirical mode decomposition (EMD), has been proposed for processing EEG signals acquired from volunteer subjects for characterizing MI activities and activity identification.