EEG Wheelchair
Brain-Controlled Navigation System
A revolutionary brain-controlled wheelchair navigation system that uses EEG (electroencephalography) signals to enable hands-free wheelchair control. This project leverages my background in EEE, medical electronics, sensors, and motor control to create an assistive technology solution for individuals with mobility impairments.
Demo Video
Coming Soon
Tech Stack
Key Features
EEG Signal Acquisition
High-precision EEG sensors that capture brain signals with minimal noise and interference for reliable control commands.
Real-time Signal Processing
Advanced digital signal processing algorithms that filter and interpret EEG patterns in real-time for immediate response.
Motor Control System
Precise motor control interface that translates brain signals into smooth, controlled wheelchair movements.
Adaptive Learning Algorithm
Machine learning system that adapts to individual users' brain patterns for improved accuracy over time.
Safety Override Systems
Multiple safety mechanisms including obstacle detection and emergency stop functionality for user protection.
Wireless Communication
Wireless connectivity for remote monitoring, data logging, and system diagnostics.
Screenshots
EEG sensor array and signal acquisition system
Real-time EEG signal processing and pattern recognition
Integrated wheelchair control system in action
Challenges & Solutions
EEG Signal Noise Reduction
CHALLENGE
EEG signals are extremely weak (microvolts) and susceptible to electrical interference from motors and other electronic systems.
SOLUTION
Implemented advanced filtering techniques, shielded sensor design, and adaptive noise cancellation algorithms to isolate brain signals from environmental interference.
Real-time Processing Requirements
CHALLENGE
Brain-computer interfaces require extremely low latency (<100ms) to provide natural, responsive control for safety and usability.
SOLUTION
Optimized signal processing algorithms using efficient C++ implementations and dedicated real-time processing hardware to achieve sub-50ms response times.
User Training & Calibration
CHALLENGE
Each individual's brain patterns are unique, requiring personalized calibration and training for effective control.
SOLUTION
Developed adaptive learning algorithms and user-friendly calibration procedures that continuously improve accuracy with minimal training sessions.
Development Timeline
EEG System Development
Q1 2024Designed and built EEG acquisition system with noise reduction and signal amplification.
Signal Processing Implementation
Q2 2024Developed real-time signal processing algorithms for pattern recognition and command extraction.
Motor Control Integration
Q3 2024Integrated motor control systems and implemented safety mechanisms for wheelchair navigation.
User Testing & Refinement
Q4 2024Conducted user testing sessions and refined algorithms for improved accuracy and usability.
Results & Impact
What I'd Do Differently
Looking back, I would focus more on user testing early in the development process, implement better error handling from day one, and spend more time on the initial architecture planning. These learnings have shaped how I approach new projects today.