Comparative Analysis of Normal and ASD EEG Data Using Hjorth Parameters
The increasing prevalence of autism spectrum disorder (ASD) in recent decades has underscored the need for more accurate and objective diagnostic methods. Electroencephalography (EEG) holds promise as a tool in ASD neurobiological research due to its ability to capture neural oscillatory activity with high temporal resolution. This study aims to analyze the neurophysiological differences between individuals with ASD and neurotypical populations by examining EEG data using Hjorth parameters. The preprocessing stage involved using a Butterworth filter to enhance EEG signal quality by eliminating noise in the frequency range of 4-40 Hz. The Hjorth parameters, including Activity, Mobility, and Complexity, were used to assess the statistical characteristics of the EEG signals. The analysis was conducted on EEG data collected from 5 typical individuals and 5 ASD ASD subjects. Our findings reveal that the Hjorth Activity parameter is notably lower in the ASD group across all regions, with the most significant differences observed in the left and right frontal areas, where average activity measures 0.236 μV<sup>2</sup> and 0.060 μV<sup>2</sup> in ASD individuals, compared to 0.512 μV<sup>2</sup> and 0.138 μV<sup>2</sup> in the neurotypical group. However, the Hjorth Complexity and Mobility values remained consistent between the two groups, implying that the fundamental structure of the EEG signals is relatively preserved. These findings offer insights into the neurobiological mechanisms of ASD and suggest the potential use of Hjorth parameters in developing more targeted interventions for individuals with ASD.
Authors
- Yuwaldi Away (ORCID: https://orcid.org/0000-0001-6798-054X)
- Muliyadi Muliyadi
- Melinda Melinda (ORCID: https://orcid.org/0000-0001-9082-6639)
- Syahrul Gazali
Institutions
- Universitas Syiah Kuala (ID)
Publication Details
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1109/ichora65333.2025.11017066
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- Universitas Syiah Kuala