Project Overview
SyncTew is a Brain-Computer Interface (BCI) system that leverages electroencephalography (EEG) signals to control external devices in real-time. This project demonstrates how EEG signals can be classified into different mental states (attentive and relaxed) to control a car racing game without physical input devices.
About Brain-Computer Interfaces (BCI)
A Brain-Computer Interface (BCI) creates a direct communication link between the brain's electrical activity and an external device, most commonly a computer or robotic system. BCIs have potential applications in:
- Assistive technology for people with disabilities
- Gaming and entertainment
- Neurofeedback and mental health applications
- Human-computer interaction research
Technical Implementation
Hardware Components
- BioAmp EXG Pill (with JST PH 2.0 connector and header pins)
- BioAmp Cable v3
- Gel Electrodes
- Arduino Uno / Maker Uno
- Nuprep Skin Preparation Gel
Software Stack
- Arduino IDE for signal acquisition
- Python for signal processing and classification
- Machine Learning (Support Vector Machine classifier) for EEG signal interpretation
- Web-based game interface
Signal Processing Pipeline
- Data Acquisition: EEG signals are captured from the prefrontal cortex using the BioAmp EXG Pill and Arduino Uno.
- Preprocessing: Raw signals are filtered to remove noise and artifacts.
- Feature Extraction: Time-domain and frequency-domain features are extracted from the processed signals.
- Classification: A machine learning model classifies the mental state as either 'attentive' or 'relaxed'.
- Control Interface: The classified states are mapped to game controls (attentive = accelerate, relaxed = brake).
Research Methodology
Data Collection
- EEG data was collected in two distinct mental states: attentive and relaxed
- 20 minutes of data was recorded for each state
- Sampling rate: 512 Hz (512 data points per second)
Model Training
- Features were extracted from the raw EEG signals
- A Support Vector Machine (SVM) classifier was trained on the labeled data
- Model achieved approximately 85% accuracy in distinguishing between the two mental states
Results and Impact
The system successfully demonstrates real-time control of a racing game using only brainwaves. This proof-of-concept shows the potential for BCI technology in gaming and beyond. Key achievements include:
- Real-time classification of mental states with minimal latency
- Intuitive control mapping that feels natural to users
- Accessible implementation using affordable, open-source hardware
Future Directions
- Expand the system to recognize more mental states for finer control
- Develop a wireless version for greater mobility
- Create a more robust machine learning model with larger datasets
- Explore applications in accessibility and assistive technology
Acknowledgments
This project was developed as part of my B.Tech Computer Science program at VIT Bhopal University.