Brain-controlled pacman: a hand movement training game

Abstract

Advancements in brain-computer interface (BCI) technologies have enabled the decoding of motor intentions from non-invasive brain signals. However, continuous decoding of fine motor movements, particularly hand grasping, remains underexplored. This project presents Brain-Controlled Pacman, a gamified training platform that integrates real-time electroencephalography (EEG)-based decoding with interactive gameplay to facilitate hand motor control. The system leverages a pre-trained deep learning model to continuously predict fingerjoint bending angles from EEG signals captured during grasping tasks. These predictions are mapped onto the in-game actions of a Pacman character in a custom-built game, Attack on Ghost Gang, where players must collect fruit and defeat enemies using their brain-controlled hand motions. Through this platform, the project investigates the feasibility and responsiveness of EEG-driven control in a dynamic environment, while analyzing how repeated task execution affects motor performance. The game design incorporates progressive difficulty levels to examine grasp intensity under increasing task demands. By combining BCI, deep learning, and game-based interaction, this work contributes to the growing field of neurorehabilitation by offering an engaging tool for training and evaluating hand motor function