Abstract:

This project presents the design and implementation of an EEG-based brain-controlled wheelchair for physically challenged individuals.

The system utilizes pre-recorded EEG data to simulate brainwave-driven commands, allowing users to control the wheelchair's movement with minimal physical input.

The control system is built around an Arduino Uno microcontroller, which receives movement commands from a PC that processes the EEG data.

The EEG dataset used in this project contains brainwave signals that correspond to specific mental states, such as concentration or relaxation, which are mapped to control commands (forward, backward, left, and right).

These commands are transmitted to the Arduino via serial communication, and the Arduino drives the wheelchair motors accordingly using a motor driver circuit.

This approach demonstrates the feasibility of brain-computer interface (BCI) technology in assistive devices, offering an affordable and practical solution for enhancing mobility in individuals with severe motor impairments.

The use of pre-existing datasets simplifies the process, overcoming the challenges of real-time EEG signal acquisition and processing.

Experimental results show effective wheelchair control using simulated EEG commands, highlighting the potential of this technology in real-world applications.

• Develop a brain-controlled wheelchair system using pre-recorded EEG data to simulate real-world applications.
• Utilize EEG signals to detect mental states and map them to movement commands (forward, backward, left, right) for wheelchair control.
• Implement a control system using an Arduino Uno microcontroller for efficient motor control and movement.
• Demonstrate the feasibility of using EEG-based Brain-Computer Interface (BCI) technology for assisting physically challenged individuals.
• Provide an affordable and accessible assistive solution using open-source hardware and pre-existing EEG datasets.