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 modern 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 Thesis is classifying different mental tasks through the use of the electroencephalogram (EEG). EEG signals from several subjects through channels (electrodes) have been studied during the performance of five mental tasks: Baseline task for which the subjects were asked to relax as much as possible, Multiplication task for which the subjects were given nontrivial multiplication problem without vocalizing or making any other movements, Letter composing task for which the subject were instructed to mentally compose a letter without vocalizing (imagine writing a letter to a friend in their head),Rotation task for which the subjects were asked to visualize a particular three-dimensional block figure being rotated about its axis, and Counting task for which the subjects were asked to imagine a blackboard and to visualize numbers being written on the board sequentially.

vvvThe work presented here maybe a part of a larger project, with a goal 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.


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