EEG Power Analysis of Children with Autism Spectrum Disorders (ASD) Based on EIBI Curriculum Levels

Early Intervention Behavioral Therapy as a method has been shown to aid children diagnosed with Autism in adjusting behavior through Applied Behavior Analysis. While there are three levels of ABA, EIBI does not provide a concrete metric of what separates between the individual levels. The current study focuses on differentiating the electrical patterns found in EEG in children and plans to explore how EIBI can serve across the ABA spectrum. The electrodes F3, F4, C3, C4, P3, P4, O1, and O2 were used to capture the EEG signals and were utilized in estimating the power, spectral density using the Welch method. It was observed during the statistical examination that there existed differences in the results of power across the frequency band amongst the groups. The higher levels of Alpha lead us to believe that there was better emotional management. The chronic group was shown to have more prominent Delta power reflecting weakened control. Comparatively, beginning level’s theta power was found to be higher across all groups showcasing change in attention requiring tasks. Due to greater focus being placed on the lower range frequency activity there existed no noteworthy changes in the Beta and Gamma portions. These findings highlight the role of EIBI in neuromodulation in the Alpha and Delta bands, and its application in the enhancement of emotional and neurological stability. EEG is an effective measure as it quantifies EIBI outcomes. Further studies should examine the long-term effects and enhance curriculum concepts to increase the efficacy of the interventions.

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Publication Details

Journal
JOIV International Journal on Informatics Visualization
Published
2024-12-31
DOI
https://doi.org/10.62527/joiv.8.4.2211
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
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article

EEG Power Analysis of Children with Autism Spectrum Disorders (ASD) Based on EIBI Curriculum Levels

Rudi Setiawan, Elsa Efrina, Asep Ahmad Sopandi, Rahmahtrisilvia Rahmahtrisilvia et al.
1 links
JOIV International Journal on Informatics Visualization
EEG and Brain-Computer Interfaces
article

EEG Power Analysis of Children with Autism Spectrum Disorders (ASD) Based on EIBI Curriculum Levels

Rudi Setiawan, Elsa Efrina, Asep Ahmad Sopandi, Rahmahtrisilvia Rahmahtrisilvia, Grahita Kusumastuti
article en

Abstract

Early Intervention Behavioral Therapy as a method has been shown to aid children diagnosed with Autism in adjusting behavior through Applied Behavior Analysis. While there are three levels of ABA, EIBI does not provide a concrete metric of what separates between the individual levels. The current study focuses on differentiating the electrical patterns found in EEG in children and plans to explore how EIBI can serve across the ABA spectrum. The electrodes F3, F4, C3, C4, P3, P4, O1, and O2 were used to capture the EEG signals and were utilized in estimating the power, spectral density using the Welch method. It was observed during the statistical examination that there existed differences in the results of power across the frequency band amongst the groups. The higher levels of Alpha lead us to believe that there was better emotional management. The chronic group was shown to have more prominent Delta power reflecting weakened control. Comparatively, beginning level’s theta power was found to be higher across all groups showcasing change in attention requiring tasks. Due to greater focus being placed on the lower range frequency activity there existed no noteworthy changes in the Beta and Gamma portions. These findings highlight the role of EIBI in neuromodulation in the Alpha and Delta bands, and its application in the enhancement of emotional and neurological stability. EEG is an effective measure as it quantifies EIBI outcomes. Further studies should examine the long-term effects and enhance curriculum concepts to increase the efficacy of the interventions.

JOIV International Journal on Informatics VisualizationVol. 8(4)
State University of Padang (ID), Sumatera Institute of Technology (ID)
Universitas Negeri Padang
Openalex Percentile: Top 73%
EEG and Brain-Computer Interfaces
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