Autism Spectrum Disorder Prediction Using TSM-BFC and a Combined CNN–GRU Model Based on the EEG Image Dataset
Received: 16 June 2025 | Revised: 7 July 2025 and 11 July 2025 | Accepted: 13 July 2025 | Online: 12 September 2025
Corresponding author: Ambika Rani Subhash
Abstract
This work focuses on enhancing the detection of Autism Spectrum Disorder (ASD) by analyzing brain connectivity patterns, as the proper connectivity between the various brain regions is necessary for regular cognition. Neurological disease diagnostics has made extensive use of Electroencephalography (EEG). Frequency-related features have been the main focus of earlier research on the use of EEG data to diagnose ASD. Most previous studies divided the data into time slices or sliding windows. However, there is a chance that these data augmentation methods contaminate the testing with training data. This study presents a novel technique for identifying ASD using EEG data to get around this problem. Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) were used to create and implement a model for diagnosing ASD, achieving a classification accuracy of 96.4%.
Keywords:
Autism Spectrum Disorder (ASD), Electroencephalography (EEG), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), deep learningDownloads
References
A. A. R. S. Al-Salihy, "Female reproductive health trends and autism spectrum disorder prevalence between 2000 and 2024," Scientific Reports, vol. 15, no. 1, Mar. 2025, Art. no. 8507.
N. Gupta, S. Srinivasan, and M. Gupta, "Rethinking psychometric testing in autism: overcoming the challenges of comorbidity and diagnostic overshadowing," CNS Spectrums, vol. 30, no. 1, 2025.
L. E. Mash et al., "Atypical Relationships Between Spontaneous EEG and fMRI Activity in Autism," Brain Connectivity, vol. 10, no. 1, pp. 18–28, Feb. 2020.
F. J. Rajam and B. R. K. Swakkin, "MAPLE: A Novel Processing Technique for Adult Autism Prediction," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23901–23906, Jun. 2025.
E. Gkintoni, A. Vantarakis, and P. Gourzis, "Neuroimaging Insights into the Public Health Burden of Neuropsychiatric Disorders: A Systematic Review of Electroencephalography-Based Cognitive Biomarkers," Medicina, vol. 61, no. 6, May 2025, Art. no. 1003.
Y. Liu, L. Xu, J. Yu, J. Li, and X. Yu, "Identification of autism spectrum disorder using multi-regional resting-state data through an attention learning approach," Biomedical Signal Processing and Control, vol. 69, Aug. 2021, Art. no. 102833.
H. Dong, D. Chen, L. Zhang, H. Ke, and X. Li, "Subject sensitive EEG discrimination with fast reconstructable CNN driven by reinforcement learning: A case study of ASD evaluation," Neurocomputing, vol. 449, pp. 136–145, Aug. 2021.
Z. J. Peya, M. A. H. Akhand, J. F. Srabonee, and N. Siddique, "Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network," Journal of Computer Science, vol. 18, no. 8, pp. 695–704, Aug. 2022.
N. Hasan, P. Paul, and M. J. Nene, "Performance Evaluation of Neural Network Models for Autism Detection Using EEG Data," Advances in Technology Innovation, vol. 9, no. 4, pp. 287–300, Oct. 2024.
Q. M. U. Din and A. K. Jayanthy, "Automated Classification of Autism Spectrum Disorder Using EEG Signals and Convolutional Neural Networks," Biomedical Engineering: Applications, Basis and Communications, vol. 34, no. 02, Apr. 2022.
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," Computer Methods and Programs in Biomedicine, vol. 250, Jun. 2024, Art. no. 108196.
S. Peketi and S. B. Dhok, "Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition," Brain Sciences, vol. 13, no. 2, Feb. 2023, Art. no. 315.
A. Rasool, S. Aslam, Y. Xu, Y. Wang, Y. Pan, and W. Chen, "Deep neurocomputational fusion for ASD diagnosis using multi-domain EEG analysis," Neurocomputing, vol. 641, Aug. 2025, Art. no. 130353.
P. Gupta, H. Lee, A. Laxmanan, M. Khadtare, P. Dharmale, and D. Ahire, "Automated Recognition of Autism Spectrum Disorder from EEG Signals Using a CNN-LSTM Hybrid Model," in 2025 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, Jan. 2025, pp. 1–6.
N. Prova, "Explainable AI-Powered Multimodal Fusion Framework for EEG-Based Autism Spectrum Disorder Classification." Elsevier BV, 2025.
S. Saranya and R. Menaka, "A Quantum-Based Machine Learning Approach for Autism Detection Using Common Spatial Patterns of EEG Signals," IEEE Access, vol. 13, pp. 15739–15750, 2025.
M. Simões et al., "BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces," Frontiers in Neuroscience, vol. 14, Sep. 2020.
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