Classification of EEG Signals in a Brain-Computer Interface System

Electroencephalography (EEG) equipment are becoming more available on the public market, which enables more diverse research in a currently narrow field. The Brain-Computer Interface (BCI) community recognize the need for systems that makes BCI more user-friendly, real-time, manageable and suited for people that are not forced to use them, like clinical patients, and those who are disabled. Thus, this project is an effort to seek such improvements, having a newly available market product to experiment with: a single channel brain wave reader. However, it is important to stress that this shift in BCI, from patients to healthy and ordinary users, should ultimately be beneficial for those who really need it, indeed. The main focus have been building a system which enables usage of the available EEG device, and making a prototype that incorporates all parts of a functioning BCI system. These parts are 1) acquiring the EEG signal 2) process and classify the EEG signal and 3) use the signal classification to control a feature in a game. The solution method in the project uses the NeuroSky mindset for part 1, the Fourier transform and an Artificial Neural Network for classifying brain wave patterns in part 2, and a game of Snake uses the classification results to control the character in part 3. This report outlines the step-by-step implementation and testing for this system, and the result is a functional prototype that can use user EEG to control the snake in the game with over 90% accuracy. Two mental tasks have been used to separate between turning the snake left or right, baseline (thinking nothing in particular) and mental counting. The solution differentiates from other appliances of the NeuroSky mindset that it does not require any pre-training for the user, and it is only partially real-time.