Problem
Brain-Computer Interfaces (BCI) offer incredible potential for controlling physical systems. The challenge was to interpret electrical brain activity (EEG signals) and translate it into a reliable mechanical output—specifically, controlling the wheel of a small vehicle.
Motivation
I'm fascinated by the intersection of biological systems and computational hardware. Building a system that can interpret human thought (even at a primitive level) and convert it into physical action is a step toward understanding neural architectures and real-time signal processing.
Approach
I researched and developed the software component that bridges the gap between the EEG device and the car's hardware controller.
The core flow:
- Signal Acquisition: Receive raw data from the EEG headset.
- Filtering & Processing: Noise reduction and feature extraction to identify specific mental states (e.g., concentration or relaxation).
- Command Translation: Mapping the identified states to hardware commands.
- Execution: Transmitting the command to the car's motor controller to move the wheels.
Currently, the system is constrained to only moving the wheel when the EEG device sends the appropriate signal, serving as a successful proof-of-concept for the signal translation pipeline.
Challenges
Raw EEG data is incredibly noisy. Distinguishing between actual cognitive signals and artifacts (like eye blinks or muscle movement) required careful filtering. Additionally, minimizing the latency between the mental command and the physical movement of the wheel was a significant engineering hurdle.
Results
Successfully created the software bridge. The car's wheels respond accurately to the processed EEG signals, proving that the signal acquisition and translation pipeline works as intended.
Future Exploration
This project is a stepping stone. Future iterations would involve expanding the command set (steering, speed control) and improving the robustness of the signal classification algorithms, potentially using machine learning models trained on custom datasets.