A novel approach to ASD detection using intuitionistic fuzzy sets and graph convolutional networks

Detection of ASD Using DySTEP-GAT Architecture With EEG-Based Attention-Fused Brain-Net Graph Representation

Electroencephalography (EEG)-based assessment of autism spectrum disorder (ASD) remains challenging due to the nonstationary, high-dimensional, and heterogeneous nature of neural signals, as well as the difficulty of jointly modeling spatiotemporal dynamics and functional connectivity patterns in a unified framework. Existing approaches often rely on either single-domain features or static graph representations, limiting their ability to capture dynamic neural interactions associated with ASD. To address these limitations, this work proposes a novel EEG-based functional brain network framework with graph learning for automated ASD detection. The framework comprises two key components: 1) a dual-attention-fused Brain-Net graph representation that models EEG-derived functional connectivity and 2) a dynamic spatiotemporal edge progression graph attention (DySTEP-GAT) network for robust classification. Resting-state EEG signals acquired from multiple cortical regions are transformed into multiband functional connectivity matrices, forming refined Brain-Net graphs that encode node-level spatiotemporal characteristics and edge-level interchannel synchronization. A semantic similarity-driven dual-attention mechanism is then employed to fuse rhythm-specific connectivity information into unified functional brain network representations. The proposed DySTEP-GAT incorporates three measurement-centric modules—dynamic edge selection block (DESB), spatiotemporal GAT block (STGATB), and progressive graph embedding block (PGEB)—to effectively model nonstationary neural coupling patterns associated with ASD. Experimental validation on two publicly available EEG datasets demonstrates that the proposed framework achieves high ASD detection accuracy (96 %–97 %), along with improved precision (0.95) and $F1$ -score (0.95) compared to recent state-of-the-art methods. Ablation studies further confirm the effectiveness of attention-based multiband fusion. These findings indicate that the learned brain network representations capture distinct and quantifiable abnormalities in ASD-related neural dynamics, establishing the proposed framework as a robust, interpretable, and measurement-aligned tool for objective ASD assessment.

Cross-attention-guided subject-adaptive graph learning for multimodal autism classification: integrating structural and functional MRI data

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by structural atypicality and abnormal functional connectivity. It remains challenging to accurately delineate an ASD-associated neural marker due to individual heterogeneity and multi-site data variability. To address these issues, we propose a cross-attention-guided subject-adaptive graph network (CAS-GNN) model that integrates structural MRI and resting-state functional connectivity data, effectively fusing complementary multimodal information. By modeling individualized brain network topologies and incorporating a site-invariant learning strategy, our approach enhances discriminability and cross-site generalization. On the ABIDE-I dataset, CAS-GNN significantly outperformed machine learning baselines and achieved an accuracy of 79.25% ± 4.71% on independent test data and an average accuracy of 78.75% ± 1.56% on five-fold cross-validation. Exploratory analyses identified key ASD-related brain regions and connections, revealing a notable right-hemisphere dominance consistent with atypical asymmetry in ASD. Our framework offers valuable neurobiological insights and provides a promising tool for interpretable and robust ASD diagnosis, accelerating biomarker discovery and development.

Investigating the efficacy of graph convolutional, gated graph, and message passing neural networks for autism spectrum disorder diagnosis

Early Autism Spectrum Disorder Detection Using Adaptive Fused Spatial–Temporal Graph Convolutional Network Optimized With Artificial Lemming Algorithm

Autism spectrum disorder (ASD) is characterized by diversity of behavioural abnormalities, and successful intervention depends on an early diagnosis. Conventional diagnostic techniques depend on interviews and observational evaluations, which can occasionally result in errors. In order to enhance the accuracy of ASD recognition, we propose an early ASD detection using Adaptive Fused Spatial–Temporal Graph Convolutional Network Optimized with Artificial Lemming Algorithm (AFSTGCN‐ALA). Initially, ASD‐related data are collected from ASD Dataset and Adaptive Fast Desensitized Kalman Filter (AFDKF) preprocessing; it effectively eliminates impulsive noise to enhance the accuracy of the data. The Multi‐Synchro Squeezing Transform (MSST) is then used to extract features from the preprocessed data, such as connections and frequency band coherence. To differentiate between instances with and without ASD, these extracted traits are then categorized using the AFSTGCN. However, adaptive optimization for parameter tweaking is absent from traditional AFSTGCN, which might affect the accuracy of detection. In order to solve this, the ALA was introduced, which optimizes the weight parameters of AFSTGCN to guarantee correct ASD categorization. The proposed AFSTGCN‐ALA model is evaluated using performance criteria such as compute time, F1‐score, accuracy and precision. According to experimental data, the suggested technique outperforms current approaches in terms of accuracy by 18.97%, 24.57% and 32.68% while cutting down on computing time by 19.84%, 24.93% and 31.62%. These results open the door to more accurate diagnosis and prompt therapies by demonstrating the effectiveness and dependability of AFSTGCN‐ALA for early ASD identification.

Data‐Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets

The need to develop large, well‐balanced datasets, the application of explainable AI techniques, standardization and regulatory guidelines for facilitating the clinical translation of ASD detection systems are suggested.

Early detection of autism spectrum disorder through hybrid deep learning and classical machine learning approaches

These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool and will focus on expanding dataset diversity and integrating multimodal behavioral cues to improve model generalization and clinical applicability.