EEG and Head Movement Controlled Wheelchair

Abstract

Mobility impairment due to neurological disorders, spinal cord injuries, or muscular dystrophy significantly affects an individual’s independence and quality of life. Conventional wheelchairs, which relyonmanual operationor joystick controls, maynotbesuitableforuserswith severedisabilitieswholackthe required motor functions. To address this challenge, this project presents an EEG and head movement-controlled wheelchair, whichprovidesahands-free and intelligentmobilitysolution.The proposed system integrates brainwave (EEG) signals to detectthe user’s consciousness and head movements to control the wheelchair’s direction. The dual-input approach ensures that userswith varyingdegreesofmotorimpairmentcanoperate the wheelchair without physical effort. Additionally, an obstacle detection system enhances safety by preventing collisions. The fusionofbrainwave analysis,motionsensing,andintelligentnav- igation makes this system a significant advancement in assistive mobility technology. The system has been tested for accuracy, safety, and real-time response.

Introduction

Overview

The paper presents a hands-free, assistive wheelchair controlled through a combination of EEG (electroencephalogram) signals and head movement tracking. It aims to improve independent mobility for individuals with severe motor disabilities who cannot operate traditional or manual wheelchairs. The system also incorporates obstacle detection to enhance safety.

Key Features

• EEG Integration: Detects brainwave patterns to interpret user intent.

• Head Movement Tracking: Uses an MPU6050 sensor to detect head tilts for directional control.

• Obstacle Avoidance: Ultrasonic sensors prevent collisions by identifying obstacles.

• Control System: An ESP32 microcontroller processes input signals and drives motors via an L293D motor driver.

• User Feedback: A 16x2 LCD display provides real-time status updates.

Related Work

• Prior research has explored EEG-based control (Casali et al., 2013), motion-sensing systems (Sathya & Ramakrishnan, 2018), and sensor fusion techniques (Li et al., 2019).

• These studies show that combining EEG and motion sensing improves control accuracy and reduces unintended movements.

System Design & Hardware Components

1. ESP32 Microcontroller – Processes EEG and head motion inputs.

2. EEG Sensor – Captures brainwave signals.

3. MPU6050 Gyroscope/Accelerometer – Detects head orientation and movement.

4. Ultrasonic Sensor – Identifies nearby obstacles.

5. L293D Motor Driver – Controls wheel motors.

6. DC Motors – Provide propulsion for the wheelchair.

7. LCD Display (I2C) – Shows system and obstacle status.

8. Rechargeable Battery & Power Circuit – Powers all components.

9. Bluetooth Module – Enables wireless communication.

10. Wheelchair Frame – Physically integrates all hardware.

Conclusion

This project presents an innovative assistive mobility solu- tion integrating EEG and head movement control. The system offersahands-freealternativetotraditionalwheelchaircontrol, enhancingaccessibilityforindividualswithseveredisabilities. Future improvements include AI-based signal interpretation, autonomous navigation, and cloud connectivity for remote monitoring. Additionally, machine learning models can be integrated to improve EEG signal processing and prediction accuracy.Enhancedsafetyfeatures,suchasemergencybraking and terrain adaptability, could further improve the system’s robustness.

References

[1] A. Casali et al., ”A theoretically based index of consciousness in-dependent of sensory processing and behavior,” Science TranslationalMedicine, vol. 5, 2013. [2] A. Sathya and S. Ramakrishnan, ”Development of an inertial sensor-based smart wheelchair navigation system,” Biomedical EngineeringLetters, vol. 8, 2018. [3] D. Li et al., ”A sensor fusion-based approach for improving wheelchairmotion control,” IEEE Sensors Journal, vol. 19, 2019.

Copyright

Copyright © 2025 Simi M S, Anjana S, Arun A Nair, Darshan Sunil Patil, S Fathima. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.