DEVELOPMENT OF BRAIN-BASED SMART-HOME/TYPING SYSTEM

Abstract

Our project combines EEG-EOG signals to develop an efficient Brain-Computer Interface (BCI) spelling system for Virtual Reality (VR) and Mixed Reality (MR) environments. This hybrid speller enables users to spell using brain activity by leveraging multi-modal signals and various classification strategies. Aimed at improving the quality of life for individuals with motor disabilities, such as spinal cord injuries, ALS, locked-in syndrome, and the elderly, our BCI system provides an alternative communication channel. Focusing on the well-established P300 Row-Column (RC) speller paradigm, we incorporate convolutional neural network (CNN) classification techniques for enhanced performance. Additionally, we use mixed reality glasses to improve user comfort and EEG signal quality. Our methodology includes comprehensive experimental procedures, from environment setup to data analysis and iterative refinement. By advancing BCI technology and integrating VR and MR interfaces, our project seeks to promote accessibility and inclusivity, enabling individuals of all abilities to communicate and participate more fully in social, educational, and professional activities.