Paper Information

Title: 

DEVELOPING THE AVERAGE FREQUENCY EXTRACTION OF EEG SIGNALS METHOD IN MATLAB SOFTWARE ENVIRONMENT

Type: POSTER
Author(s): ARAB ATIEH*,SETAREHDAN S.K.,KADKHODAEI MEHRI
 
 *DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING, UNIVERSITY OF TEHRAN, TEHRAN, IRAN
 
Name of Seminar: IRANIAN CONGRESS OF PHYSIOLOGY AND PHARMACOLOGY
Type of Seminar:  CONGRESS
Sponsor:  PHYSIOLOGY AND PHARMACOLOGY SOCIETY, MASHHAD UNIVERSITY OF MEDICAL SCIENCE
Date:  2009Volume 19
 
 
Abstract: 

Background: Nowadays, EEG signals representing the electrical function of neurons in different parts of the human or animal brains, are the most powerful tool for diagnosis and research on the function of the brain and neurological system. The traditional method of observing the raw EEG signals is still the most common method used for analysis of these signals but it cannot be used for long time recorded EEG and its observation results are extremely dependent on the observer. The frequency components of the EEG signal and its variations in time are the most important variables for analyzing the signal, as for each part of the brain due to its function produces an electrical wave within a specific frequency range and the EEG signal is a summation on all these waves over the brain.
Methods: In this paper a new method for analyzing and showing the information of EEG signals named the average frequency extraction method developed in MATLAB softwareenvironment is introduced.
Results: In this new developed method, the frequency contents of the EEG signal; the maximum and average frequency are calculated using Fast Fourier transform in a selected portion of the signal, with the help of a specified length time window which slides forward through the time axis of the selected portion of the signal, and shown as color spectrums in which the variations in the color are proportional to the variations of the signals frequency parameters throughout time. The time window length and portion of the signal to be analyzed at a time can be altered by the user, allowing the capability of compressing the output spectrum in parts of the signal with slow frequency content variation as to fasten the analysis.
Conclusion: The new developed method is a user friendly program that extracts and shows the frequency variations in a EEG signal in time providing the means of a more precise and fast analysis.

 
Keyword(s): EEG SIGNALS, FREQUENCY ANALYSIS, MATLAB SOFTWARE
 
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