## 3 Results
### Development Virtual Drone  
Using the obtained results, a virtual drone (3D avatar simulation) was controlled with the help of acquired brain waves and analyzed with the proposed algorithm. Based on inertial XYZ-world coordinates of the drone's center of mass and Euler rotation angles (roll rotation about x axis, pitch rotation about y axis and yaw rotation about z axis) obtained by a mathematical model, and in accordance with the simulated model, we also developed a 3D animation of the behavior of the drone in LabVIEW 3D animation as presented in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Right Hand** & **Hybrid NN-C3** & **Hybrid NN-CP3** & **Hybrid NN-P3** \\ \hline St 1 & Imagery & 95\% & 95\% & 92\% \\ St 2 & Imagery & 95\% & 86\% & 91\% \\ St 3 & Imagery & 91\% & 95\% & 95\% \\ St 4 & Imagery & 95\% & 95\% & 95\% \\ St 5 & Imagery & 86\% & 95\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Hybrid RBF MLP-RNN classifier results for motor imagery right hand.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Left Hand** & **Hybrid NN-C4** & **Hybrid NN-CP4** & **Hybrid NN-P4** \\ \hline St 1 & Imagery & 91\% & 95\% & 95\% \\ St 2 & Imagery & 91\% & 95\% & 91\% \\ St 3 & Imagery & 95\% & 95\% & 95\% \\ St 4 & Imagery & 95\% & 95\% & 91\% \\ St 5 & Imagery & 95\% & 95\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hybrid RBF MLP-RNN classifier results for motor imagery left hand.  
To create a BCI connection between the developed EEG sensors (acquisition headset) and the virtual avatar quadcopter, we used the developed software interface based on motor imagery tasks, capturing the EEG signals from the BCI headset and turning them into computer commands. We trained four distinguished mental tasks, one for each direction in which we proposed to use the virtual quadcopter.  
Figure 17 shows the acquired EEG signals specific to the sensors used: C3, CP3 and P3, corresponding to the motor imagery of the right hand and C4, CP4 and P4, corresponding to the motor imagery of the left hand.  
Figure 18a-d shows the BCI simulation results for the virtual quadcopter.  
Figure 16: 3D animation of the behavior of the drone in LabVIEW 3D animation.  
Figure 17: EEG signals used: C3, CP3 and P3, corresponding to the motor imagery of the right hand and C4, CP4 and P4, corresponding to the motor imagery of the left hand.  
Figure 18: BCI simulation results for the virtual quadcopter; (**a**) start position, (**b**) activate drone, (**c**) move to left and (**d**) move to right.  
The control of an aerial drone through biopotentials is an important point in the development of robotic systems. In robotics, human-robot interaction is seen with the most interest, especially in medical applications, dedicated to people with disabilities and for increasing low cognitive performance.  
The brain-computer interface is a relatively new and reliable technology that can play an important role in various fields. Through this research, we proposed an unconventional method of controlling a drone directly by brain signals obtained by a BCI system. To achieve the proposed goal, we developed a mathematical model and we simulated the operation in the MATLAB-LabVIEW virtual development environment for the virtual quadcopter and the proposed BCI architecture. We developed a 3D animation of the behavior of the avatar quadcopter in the LabVIEW environment based on the presented algorithms. The application in its current form can be used in the medical field for the recovery of patients with various neurological disorders, for increasing low cognitive performance and increasing the drone driving skills of military personnel.