Classification of EEG Signals in a Brain-Computer Interface System

• Main Author: Larsen, Erik Andreas
• Format: Others
• Language: English
• Published: Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap 2011
• Subjects: ntnudaim:6288; MTDT datateknikk; Intelligente systemer
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13578

Electroencephalography (EEG) equipment are becoming more available on thepublic market, which enables more diverse research in a currently narrow field.The Brain-Computer Interface (BCI) community recognize the need for systemsthat makes BCI more user-friendly, real-time, manageable and suited for peoplethat 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 availablemarket 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 ordinaryusers, should ultimately be beneficial for those who really need it, indeed.The main focus have been building a system which enables usage of the availableEEG device, and making a prototype that incorporates all parts of a functioningBCI system. These parts are 1) acquiring the EEG signal 2) process and classify theEEG signal and 3) use the signal classification to control a feature in a game. Thesolution method in the project uses the NeuroSky mindset for part 1, the Fouriertransform and an Artificial Neural Network for classifying brain wave patterns inpart 2, and a game of Snake uses the classification results to control the characterin 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 snakein the game with over 90% accuracy. Two mental tasks have been used to separatebetween turning the snake left or right, baseline (thinking nothing in particular)and mental counting. The solution differentiates from other appliances of the NeuroSkymindset that it does not require any pre-training for the user, and it is onlypartially real-time.