  The brain computer interface (BCI) systems are utilized for transferring
information among humans and computers by analyzing electroencephalogram (EEG)
recordings.The process of mentally previewing a motor movement without
generating the corporal output can be described as motor imagery (MI).In this
emerging research field, the number of commands is also limited in relation to
the number of MI tasks; in the current literature, mostly two or four commands
(classes) are studied. As a solution to this problem, it is recommended to use
mental tasks as well as MI tasks. Unfortunately, the use of this approach
reduces the classification performance of MI EEG signals. The fMRI analyses
show that the resources in the brain associated with the motor imagery can be
activated independently. It is assumed that the brain activity induced by the
MI of the combination of body parts corresponds to the superposition of the
activities generated during each body parts's simple MI. In this study, in
order to create more than four BCI commands, we suggest to generate combined MI
EEG signals artificially by using left hand, right hand, tongue, and feet motor
imageries in pairs. A maximum of ten different BCI commands can be generated by
using four motor imageries in pairs.This study aims to achieve high
classification performances for BCI commands produced from four motor imageries
by implementing a small-sized deep neural network (DNN).The presented method is
evaluated on the four-class datasets of BCI Competitions III and IV, and an
average classification performance of 81.8% is achieved for ten classes. The
above assumption is also validated on a different dataset which consists of
simple and combined MI EEG signals acquired in real time. Trained with the
artificially generated combined MI EEG signals, DivFE resulted in an average of
76.5% success rate for the combined MI EEG signals acquired in real-time.
