*4.3. Discussion*

The overall accuracy of 92.1% achieved by the proposed approach is considered to be rather satisfactory, especially given the fact that this rate is the result of real-time evaluation. It is also important to note that different subjects than the ones used for training were employed for this evaluation, a fact which attests to the robustness of the proposed method.

Better insight to the results can be gained by looking at the confusion matrix for all issued subject commands. It can be seen that the proposed approach not only achieves a satisfactory overall success rate, but also provides good performance per each individual movement.

Further analysis of inter-class performance shows that in 8.3% of the cases a 'reverse' command was issued, it was misclassified as a 'right' command. Moreover, the command 'left' was misclassified as a 'forward' command at a rate of 5.8% and the 'right' command as a 'reverse' command at a rate of 7.5%. This can be attributed to the fact that there is a short time delay until alpha wave amplitudes increase or decrease upon eye closing or opening, respectively. Therefore, these amplitudes are calculated into the next bit value, which can lead to errors.

A good indicator of the probability of a command being classified wrongly is the Hamming distance between each command (Table 2). Therefore, the 'forward' and 'reverse' commands are more likely to be misinterpreted into 'left' or 'right' commands and vice versa. Representing each command with more than four bits would increase the Hamming distance and, as a result, the system accuracy, but it would increase the overall recording time since the duration of every bit recording is two seconds.


**Table 2.** Hamming distances between robot commands.

The categorization of the experimental results performed according to the age of the subjects showed that the deviation in the classification accuracy of the age groups is negligible, probably because of the relatively small age difference between the two groups.

However, female subjects in the experimental procedure followed, achieved relatively higher classification accuracy than the male ones. This can be attributed to the fact that women in general exhibit greater alpha amplitudes than men [33,34].

On the other hand, although the performance of the proposed system was found to be successful, it is true that all the participants during the experiments made in this research work were healthy. Therefore, in real life conditions the e ffectiveness of experimental systems, like the one developed in this research work, is questionable because it strongly depends on the health conditions of their users who are supposed not only to be disabled persons but also having disability of various levels.

Moreover, the achievement of successful performance of a mobile robot within the territory of a controlled laboratory environment does not guarantee its e ffectiveness in real-world applications where the conditions are mostly variable and fuzzy.

Furthermore, the BCI systems that are based on a single signal may not be applicable to all users. Therefore, hybrid schemes which make combined use of various types of brain signals can be a more complex ye<sup>t</sup> even more e ffective alternative.

#### **5. Conclusions and Future Research**

The research work, presented in this paper, concerns the development of a control system which guides the motion of a mobile robot via a synchronous and endogenous EEG-based BCI, which uses the alpha brain waveforms of a human operator.

Experiments made, with the involvement of 12 subjects who had minimum training, proved that the system developed is able to guide the robotic vehicle under control in forward, left, backward, and right direction according to the eyes' blinking of its human operator. The accuracy achieved ranges from 85% up to 97.5% among the subjects while the overall accuracy was found to be equal to 92.1% for all commands. Further analysis of the experimental data related with the classification accuracy between di fferent genders and age groups showed that female subjects performed slightly better than male ones (92.9% to 91.3%, respectively), while there was just a trivial di fference detected between subjects aged from 20 to 28 years and subjects aged from 32 to 40 years (92.2% to 91.9%, respectively).

Considering both the classification accuracy achieved, by applying real-time evaluation, and the robustness evinced by the fact that subjects involved during training were di fferent than those during the experimental evaluation, it is concluded that the proposed method has the potential to be incorporated in applications such as the motion assistance to handicapped persons.

In the future, the conductors of this research work intend to experiment with hybrid BCIs where alpha brainwaves will be used along with brain signals of other type(s) such as P300 or SSVEP [35].

Moreover, task metrics, such as task completion time and path length traveled, and ergonomic metrics, such as mental workload of participants, can be additionally used for the accomplishment of multivariable evaluation of the performance of the system built [11].

Additionally, robot guidance can be assisted via additional sensors embedded into the robotic vehicle [36].

The detrimental e ffect of artifacts on EEG data can be removed by using modern algorithms that combine source decomposition with blind source separation and adaptive filtering [37].

Furthermore, enhanced performance can be achieved by applying advanced methods which have been proposed in order to add new knowledge to already learned models of robot semantic localization [38].

**Author Contributions:** All of the authors of this research article have extensively contributed to the work reported. Conceptualization, A.A., D.K., and N.K.; methodology, A.A. and D.K.; software, N.K.; validation, A.A., D.K., G.K., and N.K.; formal analysis, A.A., D.K., and N.K.; investigation, A.A., D.K., and N.K.; resources, D.K. and N.K.; data curation, A.A., D.K., and N.K.; writing—original draft preparation, A.A., D.K., and N.K.; writing—review and editing, A.A., D.K., G.K., and, N.K.; visualization, A.A. and N.K.; supervision, A.A., D.K., and G.K.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
