|
Abstract
|
Article Information:
Signal Refinement: Principal Component Analysis and Wavelet Transform of Visual Evoked Response
Ahmed Almurshedi and Abd Khamim Ismail
Corresponding Author: Abd Khamim Ismail
Submitted: August 31, 2014
Accepted: September 20, 2014
Published: January 15, 2015 |
Abstract:
|
This study presents an analysis on Visual Evoked Potentials (VEPs) recorded mainly from the occipital area of the brain. Accumulation of segmented windows (time locked averaging), Coiflet wavelet decomposition with dyadic filter bank and Principle Component Analysis (PCA) of three stages were utilized in order to decompose the recorded VEPs signal, to improve the Signal to Noise Ratio (SNR) and to reveal statistical information. The results shown that the wavelet transformation offer a significant SNR improvement at around four times compared to PCA as long as the shape of the original signal is retained. These techniques show significant advantages of decomposing the EEG signals into its details frequency bands.
Key words: Electroencephalogram, principle component analysis, Signal to noise ratio, visual evoked potentials, wavelet transforms, ,
|
|
Abstract
|
PDF
|
HTML |
|
Cite this Reference:
Ahmed Almurshedi and Abd Khamim Ismail, . Signal Refinement: Principal Component Analysis and Wavelet Transform of Visual Evoked Response. Research Journal of Applied Sciences, Engineering and Technology, (2): 106-112.
|
|
|
|
 |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
 |
| Information |
|
|
|
|
| Sales & Services |
|
|
|
|
