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Deep Learning–Based ASD Detection from EEG Signals: A Comparison of InceptionTime and XceptionTime Architectures
Corresponding Author(s) : Rachmawati Rachmawati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 4, November 2026 (Article in Progress)
Abstract
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by persistent social-communication impairments and restricted or repetitive behaviors. While clinical assessment remains the diagnostic gold standard, the demand for objective and scalable screening tools has motivated EEG-based automated classification. However, under controlled experimental settings, the relative contributions of preprocessing choices and deep learning architecture selection to ASD detection performance remain insufficiently quantified. This study systematically benchmarks artifact-aware preprocessing and model architecture by comparing InceptionTime and XceptionTime across four end-to-end processing schemes that isolate the effects of independent component analysis (ICA) and classifier design under identical segmentation and evaluation protocols. A public King Abdulaziz University EEG dataset comprising 16 subjects (8 ASD, 8 controls) was used. Signals were bandpass-filtered using a fourth-order Butterworth filter (0.5–45 Hz), optionally denoised via ICA, and segmented into 4-s windows with 50% overlap. Models were evaluated using 8-fold subject-wise cross-validation. Performance was assessed using accuracy, precision, sensitivity, specificity, and F1-score, and statistical significance was tested with the Wilcoxon signed-rank test. The Butterworth+ICA+InceptionTime pipeline achieved the best results, with a mean accuracy of 0.9886 ± 0.0046 and an F1-score of 0.9879 ± 0.0049. ICA inclusion and architecture choice yielded significant improvements in accuracy ( for both, ). These findings indicate that structured artifact suppression and multi-scale temporal modeling jointly enhance EEG-based ASD classification, supporting their use in robust clinically oriented screening systems.
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- W. Liu, K. Jia, and Z. Wang, “Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology,” Front. Neurosci., vol. Volume 18-2024, 2024, doi: 10.3389/fnins.2024.1367212.
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- F. Chollet, “Xception: Deep Learning With Depthwise Separable Convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017.
References
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H. A. Hatim, Z. A. A. Alyasseri, and N. Jamil, “A recent advances on autism spectrum disorders in diagnosing based on machine learning and deep learning,” Artificial Intelligence Review 2025 58:10, vol. 58, no. 10, pp. 313-, Jul. 2025, doi: 10.1007/s10462-025-11302-x.
J. Li et al., “Identification of autism spectrum disorder based on electroencephalography: A systematic review,” Comput. Biol. Med., vol. 170, p. 108075, 2024, doi: https://doi.org/10.1016/j.compbiomed.2024.108075.
S. Das et al., “Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review,” Prog. Neuropsychopharmacol. Biol. Psychiatry, vol. 123, p. 110705, 2023, doi: https://doi.org/10.1016/j.pnpbp.2022.110705.
A. Chaddad, Y. Wu, R. Kateb, and A. Bouridane, “Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques,” Jul. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s23146434.
W. Mumtaz, S. Rasheed, and A. Irfan, “Selecting methods for a modular EEG pre-processing pipeline: An objective comparison,” Biomed. Signal Process. Control, vol. 90, p. 105830, Apr. 2024, doi: 10.1016/j.bspc.2021.102741.
C. H. Chuang, K. Y. Chang, C. S. Huang, and T. P. Jung, “IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal,” Neuroimage, vol. 263, p. 119586, Nov. 2022, doi: 10.1016/j.neuroimage.2022.119586.
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M. Melinda et al., “Heavy–Light Soft-Vote Fusion of EEG Heatmaps for Autism Spectrum Disorder Detection,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 8, no. 1, pp. 409–429, Jan. 2026, doi: 10.35882/jeeemi.v8i1.1377.
Y. Xu, Z. Yu, Y. Li, Y. Liu, Y. Li, and Y. Wang, “Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model,” Comput. Methods Programs Biomed., vol. 250, p. 108196, 2024, doi: https://doi.org/10.1016/j.cmpb.2024.108196.
S. Y. Ke et al., “Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study.,” Front. Neurosci., vol. 18, p. 1330556, 2024, doi: 10.3389/fnins.2024.1330556.
L. Shi et al., “TFSNet: A Time–Frequency Synergy Network Based on EEG Signals for Autism Spectrum Disorder Classification,” Brain Sci., vol. 15, no. 7, Jul. 2025, doi: 10.3390/brainsci15070684.
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A. Rafiki et al., “Implementation of Vision Transformer for Early Detection of Autism Based on EEG Signal Heatmap Visualization,” Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 7, no. 1, pp. 102–112, 2025.
D. Nhu et al., “Automated interictal epileptiform discharge detection from scalp EEG using scalable time-series classification approaches,” Int. J. Neural Syst., vol. 33, no. 1, p. 2350001, Jan. 2023, doi: 10.1142/S0129065723500016.
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M. J. Alhaddad et al., “Diagnosis Autism by Fisher Linear Discriminant Analysis FLDA via EEG,” 2012.
M. Murias, S. J. Webb, J. Greenson, and G. Dawson, “Resting State Cortical Connectivity Reflected in EEG Coherence in Individuals With Autism,” Biol. Psychiatry, vol. 62, no. 3, pp. 270–273, 2007, doi: https://doi.org/10.1016/j.biopsych.2006.11.012.
E. V Orekhova et al., “Excess of High Frequency Electroencephalogram Oscillations in Boys with Autism,” Biol. Psychiatry, vol. 62, no. 9, pp. 1022–1029, 2007, doi: https://doi.org/10.1016/j.biopsych.2006.12.029.
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F. Fahmi, M. Melinda, P. D. Purnamasari, E. Elizar, and A. Rafiki, “Recognition of EEG Features in Autism Disorder Using SWT and Fisher Linear Discriminant Analysis,” Diagnostics, vol. 15, no. 18, p. 2291, Sep. 2025, doi: 10.3390/diagnostics15182291.
M. Melinda, F. H. Juwono, I. K. A. Enriko, M. Oktiana, S. Mulyani, and K. Saddami, “Application of Continuous Wavelet Transform and Support Vector Machine for Autism Spectrum Disorder Electroencephalography Signal Classification,” Radioelectronic and Computer Systems, no. 3(107), pp. 73–90, 2023, doi: 10.32620/reks.2023.3.07.
A. Li, J. Feitelberg, A. P. Saini, R. Höchenberger, and M. Scheltienne, “MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python,” J. Open Source Softw., vol. 7, no. 76, p. 4484, Aug. 2022, doi: 10.21105/joss.04484.
A. Mary Judith, S. Baghavathi Priya, and R. K. Mahendran, “Artifact Removal from EEG signals using Regenerative Multi-Dimensional Singular Value Decomposition and Independent Component Analysis,” Biomed. Signal Process. Control, vol. 74, p. 103452, Apr. 2022, doi: 10.1016/j.bspc.2021.103452.
F. Artoni and C. M. Michel, “How does Independent Component Analysis Preprocessing Affect EEG Microstates?,” Brain Topography 2025 38:2, vol. 38, no. 2, pp. 26-, Feb. 2025, doi: 10.1007/s10548-024-01098-4.
W. Liu, K. Jia, and Z. Wang, “Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology,” Front. Neurosci., vol. Volume 18-2024, 2024, doi: 10.3389/fnins.2024.1367212.
L. Cao et al., “A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain–Computer Interface-Based Stroke Rehabilitation,” Brain Sci., vol. 12, no. 11, Nov. 2022, doi: 10.3390/brainsci12111502.
H. Ismail Fawaz et al., “InceptionTime: Finding AlexNet for time series classification,” Data Mining and Knowledge Discovery 2020 34:6, vol. 34, no. 6, pp. 1936–1962, Sep. 2020, doi: 10.1007/s10618-020-00710-y.
E. Rahimian, S. Zabihi, S. F. Atashzar, A. Asif, and A. Mohammadi, “Xceptiontime: Independent time-window xceptiontime architecture for hand gesture classification,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, pp. 1304–1308, May 2020, doi: 10.1109/ICASSP40776.2020.9054586.
F. Chollet, “Xception: Deep Learning With Depthwise Separable Convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017.