Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA

Syiva ; Cut Siti Azola ; Melinda ; Melinda ; Syahrial ; Syahrial ; Rahman ; Imam Fathur ; Das ; Souvik ; Heryanto ; M. Ary
Journal of Computing Theories and Applications 📅 2026 Vol 3, No 4 🔗 DOI: 10.62411/jcta.15875
Abstract
Electroencephalography (EEG) signals are highly susceptible to noise and artifacts, which can degrade analysis accuracy, particularly in Autism Spectrum Disorder (ASD) studies. Therefore, effective preprocessing is required to improve signal quality prior to further analysis. This study proposes an integrated EEG preprocessing pipeline that combines a Finite Impulse Response (FIR) band-pass filter (0.5–70 Hz) with notch filtering and detrending, followed by temporal denoising using the Stationary Wavelet Transform (SWT) with the Daubechies 4 mother wavelet and spatial filtering based on SPHARA. This dual-domain approach is designed to address both temporal and spatial noise in multichannel EEG signals. Experimental results demonstrate that the proposed FIR combined with SWT and SPHARA pipeline consistently outperforms single-domain preprocessing methods, achieving a maximum Signal-to-Noise Ratio (SNR) of 31.93 dB. The proposed method also produces the lowest Mean Absolute Error (MAE) (16.81 µV) and Standard Deviation (SD) (0.75 µV), indicating high signal stability with minimal amplitude distortion. Root Mean Square Error (RMSE) values remain stable within the range of 29.5–592.3 µV, with a minimum RMSE of 29.5 µV, demonstrating effective noise suppression while preserving signal energy. These results confirm that integrating temporal and spatial preprocessing significantly improves EEG signal quality and supports more reliable EEG analysis for ASD-related studies.
Keywords
Autism Spectrum Disorder; Biomedical Signal Processing; EEG Preprocessing; Electroencephalography; Signal Denoising; Multichannel EEG; Stationary Wavelet Transform; SPHARA
How to Cite

Syiva, et al. (2026). Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA. Journal of Computing Theories and Applications, 3(4). https://doi.org/10.62411/jcta.15875

Syiva, Cut Siti Azola; Melinda, Melinda; Syahrial, Syahrial; Rahman, Imam Fathur; Das, Souvik; Heryanto, M. Ary, "Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA," Journal of Computing Theories and Applications, vol. 3, no. 4, 2026.

Syiva, Cut Siti Azola; Melinda, Melinda; Syahrial, Syahrial; Rahman, Imam Fathur; Das, Souvik; Heryanto, M. Ary. "Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA." Journal of Computing Theories and Applications, vol. 3, no. 4, 2026.

Syiva, Cut Siti Azola; Melinda, Melinda; Syahrial, Syahrial; Rahman, Imam Fathur; Das, Souvik; Heryanto, M. Ary. "Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA." Journal of Computing Theories and Applications 3, no. 4 (2026).

Syiva, et al. (2026) 'Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA', Journal of Computing Theories and Applications, 3(4). doi: 10.62411/jcta.15875.

Syiva, Cut Siti Azola; Melinda, Melinda; Syahrial, Syahrial; Rahman, Imam Fathur; Das, Souvik; Heryanto, M. Ary. Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA. Journal of Computing Theories and Applications. 2026;3(4).

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