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 RAM @ 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 802.11n 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