FEATURE EXTRACTION IN EEG SIGNALS FOR BRAIN COMPUTER INTERFACE

Abstract

Recent advances in computer hardware and signal processing have made possible the use of EEG signals or "brain waves" for communication between humans and computers. Locked-in patients have now a way to communicate with the outside world, but even with the last modem techniques, such systems still suffer communication rates on the order of 2-3 tasks/minute. In addition, existing systems are not likely to be designed with flexibility in mind, leading to slow systems that are difficult to improve. This M.Tech dissertation explores the effectiveness of Time — Frequency Analysis as a technique of classifying different mental tasks through the use of the electroencephalogram (EEG). EEG signals from several subjects through 10 channels (electrodes) have been studied during the performance of different mental tasks. Improved online classification of two of them (Finger movement and without finger movement), are taken which shows the good classification result. Different methods based on Time Frequency representations have been considered for the classification between the two tasks mentioned above. The results indicate that this method is able to extract in second, distinguishing features from the data that could be classified as belonging to one of the two tasks with an average percentage accuracy which tends to approximate zero. The same results were found when the method was exported for two tasks EEG signal classification. The work presented here is a part of a larger project, whose goal is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface, in order to investigate the feasibility of using different mental tasks as a wide communication channel between people and computers.