Assessing the Role of Intelligent BCI for Disabled Individuals - A Real-Time Approach and Standard BCI Architecture

This study investigates how intelligent BrainComputer Interfaces (BCIs) can empower people with disabilities by facilitating real-time control and communication through neural activity. The primary purpose of this research is to develop and implement a real-time BCI system that has minimal latency for command activation. To facilitate interaction between the many parts of the system, the proposed architecture employs an intelligent data processing and classification approach with an automotive focus. Information has been sent to the server we set up to carry out commands through a neuro-sky mind wave device. The Think-Net Convolutional Neural Network (TN-CNN) design was made to classify information by distinguishing brain signals and gathering them into six essential thinking states. To downplay system load, information is gathered and processed by the central integrated server. Excellent results are obtained by testing the deep learning model and the implemented architecture. Accurate command processing and the least amount of data loss were the suggested system integrity levels. Assistive devices operating on local and cloud servers will benefit from the suggested real-time architecture's ability to process data intelligently and with fewer errors.