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

Title:	EEG data compression
Author:	Wu, Yun-Chu
View Online:	njit-etd1991-037 ([viii], 65 pages ~ 2.5 MB pdf)
Department:	Biomedical Engineering Committee
Degree:	Master of Science
Program:	Biomedical Engineering
Document Type:	Thesis
Advisory Committee:	Reisman, Stanley S. (Committee chair) Pinkhasov, Elizabeth (Committee member) Kristol, David S. (Committee member)
Date:	1991
Keywords:	Electroencephalography. Data compression (Computer science)
Availability:	Unrestricted
Abstract:	This paper presents two different ways to compress EEC data-direct data com pression and a data transformation technique. The Adaptive Delta modulation and Huffman coding are used in the former method to predict or interpolate the data. Linear orthognal transformation algorithms are used in the latter method to detect and reduce the redundancies of the data by analyzing the spectral and energy distribution. Each method is implemented by programming the computer. The experimental results of their efficiencies and errors with different requirements and under different situations are compared and discussed. By comparing the EEC data compression degree and normalized square error, the paper shows that the adaptive delta coding followed by Huffman coding is the best way to compress the EEG data.