Honors College Theses
Publication Date
5-9-2024
Major
Computer Science (B.S.)
Document Type and Release Option
Thesis (open access)
Faculty Mentor
Dr. Rocio Alba-Flores
Abstract
This research explores the potential of using gyroscopic data from a person’s head movement to control a DJI Tello quadcopter via a Brain-Computer Interface (BCI). In this study, over 100 gyroscopic recordings capturing the X, Y and Z columns (formally known as GyroX, GyroY, GyroZ) between 4 volunteers with the Emotiv Epoc X headset were collected. The Emotiv Epoc X data captured (left, right, still, and forward) head movements of each participant associated with the DJI Tello quadcopter navigation. The data underwent thorough processing and analysis, revealing distinctive patterns in charts using Microsoft Excel. A Python condition algorithm was then developed for the gyroscopic data interpretation to determine each head movement direction in addition to using the Tello drone commands derived from Tello SDK 2.0 User Guide Library. Real-time control was achieved by integrating a Python Lab Streaming Layer (LSL) for continuous data exchange between the Emotiv Epoc X and the Tello quadcopter. Experimental results affirm the successful control of the Tello quadcopter through gyroscopic data and head movements 98% accuracy run-time, showcasing the potential of this technology in drone control.
Recommended Citation
Melton, Ikaia Cacha, "Brain Computer Interface-Based Drone Control Using Gyroscopic Data From Head Movements" (2024). Honors College Theses. 969.
https://digitalcommons.georgiasouthern.edu/honors-theses/969
