1. Deep Blue Brain Drone Introduction Brain Drone Components Purpose
Christopher Romero, Gobind Rauniyar, Henry Flowers, Luis Segura, Tyler Toth
University of North Texas: College of Engineering : Department of Computer Science & Engineering
Introduction
Brain Drone
Components
The Brain Drone project builds an interface between the Emotiv software development kit (SDK) and the drone. The EEG is calibrated to each user and is trained, through repetition, using the Emotiv control panel to train commands. Then, using that training profile, thoughts can be sent as flight commands. This is illustrated in the system state diagram below.
The Brain Drone was developed for Dr. Hassan Takabi, an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas. Dr. Takabi’s research focuses on information security and privacy.
Parrot AR.Drone 2.0
HD Camera: 720p 30 FPS
800MHz video DSP
1GB DDR2 200MHz
1GHz 32 bit ARM Cortex A8 Processor
Total weight 420/380g (indoor/outdoor hull)
Wi-Fi b/g/n
Emotiv EPOC EEG headset
14 EEG Channels + 2 References
Bandwidth: .16 – 43 Hz
Battery Life: 12 Hours (average)
4.0 Low Energy Bluetooth (2.4GHz)
Sampling Rate: 128 samples per second
Raspberry Pi Wi-Fi extender
2 Panda Wireless PAU06 300Mbps n USB Adapters
Dimensions: 8.6cm x 5.4cm x 1.7 cm
700MHz ARM1176JZF-S core
512MB SDRAM
32GB Samsung Evo+ SD Card
Purpose
The purpose of this project is to design a system where a user can effectively fly a drove using simple thoughts. To decipher thoughts, electroencephalography (EEG) technology will be used to monitor electrical signals in the brain and translate them into meaningful information. This requires creating and designing a pathway for the brain to be able to communicate, control and fly a drone. This involves:
Building a graphical user interface (GUI) using C#
Controlling a drone using Node.js
Learning complex EEG tools
Creating an extended Wi-Fi network that passes communication between the brain and the drone through a Raspberry Pi
Problems Faced
Deep Blue Team
Analyzing electroencephalography (EEG) signals in real time
Getting accurate filtered and transformed data
Generating extracted features for use in a classification algorithm
Future Work
Develop machine learning algorithm
Improve the style and feel of the user interface
Add a Wi-Fi extender attachment to drone
Display real time graph of the EEG data in the user interface
Improve the mental training process
Deep Blue [L to R]: Tyler Toth, Gobind Rauniyar, Luis Segura, Henry Flowers, and Christopher Romero