Event-Related Potentials Extraction of Working Memory Using Wavelet Algorithm

Abstract

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.

Logistic regression of working memory impairments in children based on single-trial ERP features

“…1). The EEG spectrum generated were within five frequency bands, i.e., delta (< 4 Hz), theta (4 -8 Hz), alpha (8 -13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (> 30 Hz) [5]. Fast Fourier Transform (FFT) is not suitable for frequency decomposition because it is sensitive to noises, and in this case, the EEG signal is non-stationary (complex signals).…”

“…Such ERPs have their changes time-locked to a defined event, which could be detected before or immediately after stimuli. Analysis concerning the ERPs includes pre-processing; averaging the data time-locked to the stimulus or response; and averaging over the subjects for substantial properties [13]. This kind of ERP technique can take in raw EEG segmented data to investigate cognitive processing.…”