Mind-Controlled Mobility Solutions Utilizing Electroencephalogram-Based Brain-Machine Interfaces

This research project focuses on the innovative development of mind-controlled mobility solutions using electroencephalogram (EEG) signals through a non-invasive Brain-Machine Interface (BMI) to empower individuals with mobility impairments. By incorporating both synchronous and asynchronous Brain-Computer Interface (BCI) methodologies, this study aims to enhance the accuracy and efficiency of brain-controlled systems through the analysis of multiple brain signals to refine user intent interpretation for seamless control over mobility devices. A key aspect of the project is the implementation of shared control mechanisms to enhance system security, enabling collaborative interaction between users and devices for safe and reliable operation. Machine learning classifiers, including support vector machines, artificial neural networks, and decision trees, has been be evaluated for real-time processing of EEG signals to determine the most effective algorithms for translating brain activity into commands that facilitate essential movements like moving forward, backward, and turning. This systematic approach will involve signal acquisition, preprocessing, classifier training, and user testing to validate system efficacy. Ultimately, this project seeks to bridge the gap between neurotechnology and practical mobility applications, contributing valuable insights to neuroscience, engineering, and rehabilitation while enhancing the usability and accessibility of assistive devices to improve the quality of life for individuals with physical disabilities.