The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Automated EEG spectrum analysis system
Author:	Laguna, Marinela C.
View Online:	njit-etd1985-006 ([13], 229 pages ~ 8.2 MB pdf)
Department:	Biomedical Engineering Committee
Degree:	Master of Science
Program:	Biomedical Engineering
Document Type:	Thesis
Advisory Committee:	Reisman, Stanley S. (Committee chair) Kristol, David S. (Committee member) Meola, Robert R. (Committee member)
Date:	1985
Keywords:	Electroencephalography--Data processing
Availability:	Unrestricted
Abstract:	A computer system has been designed to acquire EEG data from monkeys and to perform the spectral and statistical analysis. The system is capable of processing two channels simultaneously, acquire 64 Kbytes of EEG data for each channel on floppy disk, do the spectrums and averages of the spectrums, and calculate the histogram amplitudes of 13 frequency bands in a range of 0-71.1 Hz. The software gives the user a lot of choices with respect to data acquisition and processing. The relative spectral power differences between "no-drug" and "drug" experiments give information about the drug effects on the central nervous system. The present system is easier to control with respect to previous similar systems because it is capable of performing the experiments in an automated manner without any human intervention. Another quality of the system is the simplicity and small space needed. Being based on software, the system is very flexible and easy to extend. One of the features of the new system is new programs for the statistical comparisons can be done automatically. Another feature is related to the extension of the system to process 15 channels simultaneously. Experiments have been performed to validate the system using the previous system results. Results are presented to show the quality of the system. Investigations have been done into other ways of analyzing the spectral data in order to improve the system. The results of this system can be successfully used in classifying new drugs.