Revolutionizing Human-Computer Interaction through Brain-Machine Interface: An EEG-Based Approach

Abstract

Human-Machine Interface (HMI) devices, such as keyboards and mice, have been the primary means of input for computers since their inception. However, these devices have limitations that make them unsuitable for individuals with motor disabilities, motor impairment, or diseases such as paralysis, muscular dystrophy, polio, cerebral palsy, and others. This limits their ability to fully engage in computer-related activities, which can have a significant impact on their quality of life. This research paper proposes that new and emerging technologies, such as Brain-Machine Interface (BMI) and Machine Learning (ML), could be utilized to design a more convenient and accessible HMI solution that improves the quality of life for individuals with physical disabilities. BMI technology enables communication between the brain and external devices, while ML can analyse data and make predictions based on patterns and models. Combining these technologies can provide a more intuitive and adaptive interface that can detect and respond to the user's intentions and needs