A Novel Approach for Non-Invasive, Brain-Computer Interaction Using Electroencephalogram Signals

This paper presents a novel approach for non-invasive, brain-computer interaction using signals acquired from an end user. The proposed system employs a hybrid Machine Learning (ML) model to analyze EEG (Electroencephalogram) signals from the user’s brain, which are then digitized, processed, and mapped to appropriate outputs, ultimately enabling users to control a video game environment and play a game without needing any physical inputs. This paper also aims to develop a video game that seamlessly integrates with and masks the limitations of a BCI. This innovative approach opens up new possibilities for human-computer interaction, offering an intuitive and efficient means of control. By using P300 signals along with mu rhythms, the system maximizes functionality and provides a seamless and immersive user experience. The non-invasive nature of the sensors ensures comfort and ease of use.