Improvement for facial gestures classification to control a drone

Over the years, unmanned aerial vehicles have had significant development and impact on different activities of the human being, such as mining, agriculture, health, military, entertainment, among others. Therefore, researchers have sought to merge this technology with others that allow it to improve its performance, make it accessible, and solve specific problems that currently cannot. One of these technologies is brain-computer interfaces, which can manipulate devices through brain signals. In this work, an Artificial Neural Network is proposed together with data preprocessing to improve the recognition of the gestures used to control an unmanned aerial vehicle. Two test algorithms, 10-fold Cross Validation and Hold Out, were applied and the efficiency results obtained were 94.14% and 98.95%, respectively.