EVENT-RELATED POTENTIALS EXTRACTION OF WORKING MEMORY USING WAVELET ALGORITHM
SitiZubaidahMohd Tumari, Rubita Sudirman and Abdul Hamid Ahmad
DOI : 10.3844/jcssp.2014.264.271
Journal of Computer Science
Volume 10, Issue 2
This study was designed to classify and determine the Event-Related Potentials (ERPs) signal pattern of normal children on visual response. Thirty-eight children aged between 10 to 12 years old were subjected to a two-phase computer-based assessment while their working memory activity was recorded using a Neurofax-EEG 9200 machine. For children, it is anticipated that some information can be lost when there is too much information given at any one time due to limited memory capacity and this is a type of memory impairment. Based on the visual stimulus responses, EEG signal were recorded and captured from channel location at Fz. This paper explains the extraction of raw EEG signals into grand mean ERPs signal which to determine the pattern of signal developed. The ERPs concerning latency and amplitude variability of the P300 component was evaluated. The analysis was based on Discrete Wavelet Transform (DWT) algorithm and focused on alpha rhythm. Results indicated that the Daubechies wavelet at a decomposition level of 4 (db4) was the most suitable wavelet for pre-processing raw EEG signal of working memory. A significant increase of latency was detected in children aged 10 to 12 years old at channel Fz (frontal midline) when the visual stimuli became more difficult. For amplitude variability, the girls gave higher amplitude at Phase 1. These results supported the concept of increased cognitive memory in children.
© 2014 SitiZubaidahMohd Tumari, Rubita Sudirman and Abdul Hamid Ahmad. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.