Identification of working memory impairments in normal children using wavelet approach

Tumari, Siti Zubaidah Mohd; Sudirman, Rubita; Ahmad, Abdul Hamid

doi:10.1109/isiea.2012.6496653

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…To the best of our knowledge, very few studies employed the non-invasive EEG to monitor the working memory status and used power spectral density (PSD) to analyse features of the EEG signals with working memory information. Tumari et al (2012) identified the working memory impairment using Wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

Spectral Analysis of EEG Signals during Working Memory Tasks

Naushad¹,

Mansor²

2021

IJARBSS

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Assessment of the working memory performance is important to assist in the design of an effective pedagogical technique that suits the requirements of each learner. Most researchers used bulky equipment and an invasive technique to study working memory performance. The use of non-invasive electroencephalogram (EEG) to examine the working memory status directly from the brain is very limited. This paper describes the analysis of EEG signals during working memory tasks using power spectral density to identify the suitable frequency range for examining the working memory performance. The recorded EEG signals were filtered to remove the unwanted signals. Power spectral density was computed on four frequency band signals to identify the signal strength at each location on the scalp and to examine the brain active regions. Results from the analysis showed that the low-frequency theta could reveal the working memory status for all activities. The findings in this work can be used to develop an EEG-based Working Memory Assessment system that can assist educators to strategise in helping learners to succeed academically.

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Section: Introductionmentioning

confidence: 99%

Spectral Analysis of EEG Signals during Working Memory Tasks

Naushad¹,

Mansor²

2021

IJARBSS

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“…The prefrontal cortex area of the brain records the performance of our working memory (Tumari et al, 2012). It is at the anterior part of the brain as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Event-Related Potentials Extraction of Working Memory Using Wavelet Algorithm

Tumari

Sudirman²,

Ahmad³

2014

Journal of Computer Science

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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.

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“…After that, these children had their prefrontal cortex region's EEG signal capture using a medical device called Neurofax 9200 EEG with 10-20 Electrode Placement protocol. The prefrontal cortex is the area of the brain that governs our emotion, complex thought, and problem solving [4].…”

Section: Introductionmentioning

confidence: 99%

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

Tumari

Sudirman

2014

2014 2nd International Conference on Technology, Informatics, Management, Engineering &Amp; Environment

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The purpose of this study is to determine the performance of the working memory in processing visual stimuli based on the single-trial Event-Related Potential (ERP) signal. This signal was further analyzed using the logistic regression method to investigate children's working memory ability. This study involved 54 children aged 10 to 12 years old who were given computer-based visual stimuli while their working memory activities were simultaneously recorded using the Neurofax 9200 EEG acquisition machine. Redundant noises and artifacts were removed using wavelet algorithm and ICA techniques. The EEG signals were segmented to obtain the average mean of single-trial ERP and necessary input for the logistic regression classifier. Results indicated that the resultant parameters as well as biological data were significant in predicting whether a child had working impairment or not. Moreover, the signal's sensitivity value was more than the specificity value. In conclusion, our results indicated the working memory impairment in children tends to worsen with age.

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Identification of working memory impairments in normal children using wavelet approach

Cited by 7 publications

References 17 publications

Spectral Analysis of EEG Signals during Working Memory Tasks

Spectral Analysis of EEG Signals during Working Memory Tasks

Event-Related Potentials Extraction of Working Memory Using Wavelet Algorithm

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

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