Project Overview
This project aims to develop an artificial intelligence model that interprets EEG signals through a brain-computer interface (BCI) to understand human intent and translate it into output that affects drone behavior.
The model will be given datasets of EEG recordings that capture the brain activity of individuals performing deterministic and hesitant drone operations.
Using this data, the model will learn to recognize brain patters that will allow it to determine the confidence level of the user's intent and generate an output that affects a drone's behavior based on the confidence level of the pilot.
This is a sophomore research project under Huixin Zhan through Zhan Lab at New Mexico Tech.
Key Features
- BCI integration
- Real-time data processing
- Real-time drone behavior control
- Confidence-based decision making
Development Process
This project was created with Python. It utilizes MNE-Python, Braindecode, and MOABB for understanding the funtions of EEG readings and processing.