Operating of a Drone Using Human Intent Recognition and Characteristics of an EEG Signal

Abstract

Drones are applied for normal subjects likewise as forces exercise. Consultations with drones are liable to deal with and compromise since they're broadly used for self-directed conduct. Still, it is of great consequence to take on an automatic pilot framework which is vigorous to potential digital assault. Right now, we tend to propose an individual's implicit intent recognition model dependent on a multi-modular data that is a blend of the eye movement data and the EEG signal acquired from some eye-locators and EEG scanners separately. The eye movement will be used to extricate some highlights like fixation length and fixation count relating to the visual stimuli, and similarly, we will examine the graph signals observed on part synchronicity technique and consolidating this, we will also train a few classifiers such as the SVM classifier, Naïve Bayesian and Gaussian Mixture Model that might effectively recognize an individual's implicit intention into 2 characterized classes - navigational and informational intention, which will ultimately be used for training a drone. Also, we will be displaying a biometric framework to scramble letters between a drone and an electronic ground station which can be achieved by creating a key from the EEG signal of a user. Then, at the endpoint, once the correspondence with a drone is assaulted a security system facilitates it to a sheltered ‘home’ area.