# Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence
## 4 Conclusions  
This work implements machine learning techniques to control the movement of a robotic arm by using EEG signal information gathered through EEG electrodes. Further, different classifiers are applied to the extracted data to train the system and compute the accuracy. The EEG data has been extracted from four users to apply the best fit classification algorithms for precise and accurate predictions. The proposed non-invasive has predicted a reliable bandwidth through the entire BCI process that provides an exact placement of EEG electrodes to verify different arm moments. In this study, we have used classifiers, i.e., Random Forest, KNN, Gradient Boosting, Logistic Regression, SVM, and Decision Tree. It can be concluded from the results of the novel deployment that the accuracy for Random Forest shows the best accuracy, 76%, while Decision Tree gives the lowest at 64%. The proposed prototype has great potential as it can be further extended for criminal investigations, autonomous vehicle control systems, the education of disabled students, and prosthetics. Conceptualization, J.A., A.Q., A.-U.R., M.K.N., M.S., A.U.R., E.T.E., N.A.G. and H.H. ; methodology, J.A., A.Q., A.-U.R., M.K.N. and A.U.R. ; software, A.Q., A.-U.R. and M.K.N. ; validation, J.A., N.A.G., M.S. and A.-U.R. ; formal analysis, A.Q., A.-U.R. and M.K.N. ; investigation, J.A., A.Q., A.-U.R. and M.K.N. ; resources, J.A., M.S. and A.-U.R. ; data curation, J.A. and A.U.R. ; writing--original draft preparation, J.A., A.Q., N.A.G., A.-U.R. and M.K.N. ; writing--review and editing, J.A., A.Q., A.-U.R., M.K.N., M.S., A.U.R., E.T.E. and H.H. ; visualization, A.-U.R. and M.K.N. ; supervision, J.A. and M.S. ; project administration, M.S., E.T.E. and H.H. ; funding acquisition, E.T.E. All authors have read and agreed to the published version of the manuscript. This research was supported by the Future University Researchers Supporting Project Number FUESP-2020/48 at Future University in Egypt, New Cairo 11845, Egypt. Not Applicable. The authors declare no conflict of interest. Figure 12: Screenshot of Robotic Arm movement. * [PERSON] and [PERSON] (2010) [PERSON]; [PERSON] Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces. _IEEE Trans. Pattern Anal. Mach. Intell._**2010**, _33_, 433-445. [CrossRef]
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