Description of Invention
This study presents a brain-computer interface (BCI) system that leverages ResNet Convolutional Neural Networks (CNN) to monitor brain signals in real-time. The proposed system utilizes electroencephalography (EEG) to capture brain activity, which is then processed using ResNet CNN to classify and interpret brain signals. The system aims to provide accurate and efficient brain signal monitoring, enabling applications in neurology, psychology, and assistive technologies.The ResNet CNN architecture is trained on a dataset of EEG recordings, allowing the system to learn robust features and patterns in brain activity. The system achieves high accuracy in classifying brain signals, demonstrating its potential for real-time monitoring and analysis.This study contributes to the development of advanced BCI systems, enabling innovative applications in brain- computer interaction and neuroscience research.