Brain-Power Drive with Proactive Healthcare for Mobility Aids (Update)

Project Overview
Thanks to the OpenBCI Discovery Program’s support with the EEG Cap Kit and EmotiBit, I developed NeuroStride — a brain-powered mobility aid that enables individuals with accessibility needs to control mobility devices using brain signals. The device also features built-in real-time health monitoring to detect emergencies. The project recently earned a Silver Medal at the 2025 Toronto Science Fair and is now ready for real-world testing.

Video Demo

Step-by-Step Guide: How I Built NeuroStride

Hardware Used:
EEG Cap Kit (OpenBCI Discovery Program)
OpenBCI Cyton Board
EmotiBit Sensor (for vital sign tracking)
Arduino Microcontroller (for motor control)
Motorized Wheelchair Chassis (prototype)

1. Brainwave Data Acquisition
Captured brain signals using the EEG cap and Cyton board.
Streamed and visualized EEG data with OpenBCI GUI.

2. Signal Processing
Filtered noise from EEG signals.
Removed artifacts and extracted features related to motor imagery.

3. Machine Learning Model
Trained an AI model to classify brainwave patterns for movement commands.
Used Reinforcement Learning to improve accuracy over time.

4. Health Monitoring Integration
Integrated EmotiBit to monitor heart rate, oxygen, and temperature in real-time.
Linked health monitoring alerts with mobility system control.

5. Mobility Control
Mapped classified brain signals to wheelchair movement.
Added collision avoidance with basic sensors for extra safety.

6. Testing and Iteration
Conducted trials to refine brain signal processing and system response.
Identified the need for gel-free electrodes to improve user comfort during long sessions.

What’s Next?
1. Testing with quadriplegic individuals for usability.
2. Switching to gel-free electrodes for extended use.
3. Improving AI classification speed and system adaptability.