P300 detection and characterization for brain computer interface

Abstract

Advances in cognitive neuroscience and brain imaging technologies have enabled the brain to directly interface with the computer. This technique is called as Brain Computer Interface (BCI). This ability is made possible through use of sensors that can monitor some of the physical processes that occur inside the brain. Researchers have used these kinds of technologies to build brain-computer interfaces (BCIs). Computers or communication devices can be controlled by using the signals produced in the brain. This can be a real boon for all those who are not able to communicate with the outside world directly. They can easily forecast their emotions or feelings using this technology. In BCI we use oddball paradigms to generate event-related potentials (ERPs), like the P300 wave, on targets which have been selected by the user. The basic principle of a P300 speller is detection of P300 waves that allows the user to write characters. Two classification problems are encountered in the P300 speller. The first is to detect the presence of a P300 in the electroencephalogram (EEG). The second one refers to the combination of different P300 signals for determining the right character to spell. In this thesis both parts i.e., the classification as well as characterization part are presented in a simple and lucid way. First data is obtained using data set 2 of the third BCI competition. The raw data was processed through matlab software and the corresponding feature matrices were obtained. Several techniques such as normalization, feature extraction and feature reduction of the data are explained through the contents of this thesis. Then ANN algorithm is used to classify the data into P300 and no-P300 waves. Finally character recognition is carried out through the use of multiclass classifiers that enable the user to determine the right character to spell.