EEG BASED BRAIN-COMPUTER COMMUNICATION SYSTEM

Abstract

There exists a significant population who, due to disease or injury, is totally paralyzed but have normal or near-normal brain function. In such cases, called Locked-in-Syndrome, the individual is aware of his or her surroundings, but has no way of communicating with the outside world. In cases where the person has even a slight degree of voluntary movement (e.g., eyebrow motion), it is possible to use that movement as a switch for controlling a computer. Likewise, when the person has good eye control, he or she can be fitted with an eye-tracking device to control cursor move-ment on a computer screen. In many cases, however, the individual may have no reliable voluntary motion to attach a switch to, and eye-movement may not be precise enough to use with an eye-tracking device. In such cases, the only possible method of communication would be to use electrical signals produced by the brain as a switching device for computer interaction i.e. Brain Computer Interface (BCI). A Brain Computer Interface (BCI) is a real-time communication system designed to allow, users to voluntarily send messages or commands without sending them through the brain's normal output pathways. BCI users send information by engaging in discrete mental tasks that produce distinct EEG signatures. These tasks, called cognemes, form the basis of a BCI language. In P300 BCIs, users view a display containing several stimuli, one of which is the target. Stimuli are flashed sequentially, and 11 users count target flashes, thereby conveying one of two cognemes (/flash attended/ or /flash ignored/). Only attended flashes produce robust P300s, enabling target identification via the EEG. The aim of this dissertation is to develop a Brain Computer communication system, which predicts the correct character in each of the provided character selection epochs using the P300 'event related potential. The P300 is a late positive wave that occurs between 250 and 800 milliseconds after the onset of a meaningful stimulus. Because of its robustness, the evoked electrical potential called -P300 has been used in EEG-based computer interface. In these experiments, a user focused on one out of 36 different characters. The objective in this dissertation is to predict the correct character in each of the provided character selection epochs. Detection and classification of P300 evoked potentials namely was carried out by averaging, peak picking and classification algorithms. The algorithm developed in the present study have been tested on the data sets obtained from Wadsworth Research Organization, New York, USA iii