Assistive Wheelchair Mobility Using Brain Waves and Facial Expressions: The case of home environment

This paper presents a novel non-invasive Brain-Computer Interface (BCI) system aimed at improving the independence and quality of life of individuals suffering from chronic motor disabilities such as quadriplegia and Amyotrophic Lateral Sclerosis (ALS). The proposed system enables users to control both mobility and smart home devices using a combination of mental commands and facial expressions, eliminating the need for physical movement or external assistance. The system employs the EMOTIV EPOC X EEG headset to acquire brain activity and facial gesture data. Through training sessions conducted in the EMOTIV BCI software, users can associate specific cognitive tasks (e.g., “Push,” “Pull,” “Left,” “Right”) and facial gestures (e.g., winking, raising eyebrows) with desired actions. These signals are transmitted using the Open Sound Control (OSC) protocol and received by an Arduino Uno microcontroller, which processes the commands and controls actuators accordingly. The system supports robotic wheelchair movement and the control of room conditions such as turning lights or fans ON or OFF. Experimental results demonstrate the system’s reliability, achieving a peak accuracy of 96.6% for the “raising eyebrows” facial gesture and 93.3% for the “Push” mental command, with a minimum response time of just 0.8 seconds. To enhance user safety during mobility, the system integrates a GPS module for real-time location tracking and an ultrasonic sensor for obstacle detection and collision avoidance. Unlike many existing systems that focus solely on motion control or require invasive procedures, this design offers a dual-mode, low-cost, and extensible solution suitable for deployment in low-resource settings. By combining cognitive and facial control channels with environmental and navigational awareness, the system provides a practical pathway toward greater autonomy for users with severe physical impairments.