We Hereby Recommend That the Thesis Prepared under Our Supervision by Mohammad Nayeem Teli Entitled Dimensional- Ity Reduction and Classification of Time Embedded Eeg Signals Be Accepted as Fulfilling in Part Requirements for the Degree of Master of Science

OF THESIS DIMENSIONALITY REDUCTION AND CLASSIFICATION OF TIME EMBEDDED EEG SIGNALS Electroencephalogram (EEG) is the measurement of the electrical activity of the brain measured by placing electrodes on the scalp. These EEG signals give the micro-voltage difference between different parts of the brain in a non-invasive manner. The brain activity measured in this way is being currently analyzed for a possible diagnosis of physiological and psychiatric diseases. These signals have also found a way into cognitive research. At Colorado State University we are trying to investigate the use of EEG as computer input. In this particular research our goal is to classify two mental tasks. A subject is asked to think about a mental task and the EEG signals are measured using six electrodes on his scalp. In order to differentiate between two different tasks, the EEG signals produced by each task need to be classified. We hypothesize that a bottleneck neural network would help us to classify EEG data much better than classification techniques like Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machines. A five layer bottleneck neural network is trained using a fast convergence algorithm (variation of Levenberg-Marquardt algorithm) and Scaled Conjugate Gradient (SCG). Classification is compared between a neural network, LDA, QDA and SVM for both