Brain–computer interfaces for communication and control-EEG Wheelchair Development

The project "Brain–computer interfaces for communication and control-EEG Wheelchair Development” focuses on creating an intelligent system that allows individuals with motor disabilities to control a motorized wheelchair using brain signals. By leveraging advanced machine learning techniques, the project aims to improve the accuracy and responsiveness of wheelchair control through brain activity interpretation. The system uses electroencephalogram (EEG) data to train and test models, employing key steps like signal preprocessing for noise reduction, feature extraction to detect meaningful brain patterns, and classification algorithms such as Support Vector Machines (SVM) and Neural Networks to translate brain signals into control commands. The ultimate goal is to provide a user-friendly and adaptive interface that enables real- time wheelchair navigation, offering enhanced mobility solutions for users. With applications in assistive technology and rehabilitation, this project seeks to advance brain-computer interface technology by integrating machine learning to deliver efficient, real-time control for individuals.