Aug 2026· Brain Topography· Vol 39· 0 citations· 49 references
Medicine
TL;DR
The proposed electroencephalography-based ASD detection method was evaluated against previous approaches using the same datasets, showing a substantial improvement in performance, and the need to evaluate the proposed approach on larger datasets to further ensure model generalization.
Autism Spectrum Disorder (ASD) diagnosis benefits from the technical analysis of neural oscillations. The objective identification of Autism Spectrum Disorder (ASD) is advanced through a dedicated engineering framework that analyzes neural oscillations via electroencephalogram (EEG) signals. This methodology synthesizes statistical signal processing, frequency‐domain transformation, and computational intelligence. Multi‐channel EEG data from 15 electrodes were first organized into seven functional brain regions. A Discrete Wavelet Transform (db4) was then applied for multiresolution decomposition, extracting and normalizing the spectral power of delta, theta, alpha, beta, and gamma frequency bands. This spectral characterization was augmented with statistical descriptors to quantify signal dynamics and complexity, creating a robust multi‐domain feature set. For classification, the framework implements a comprehensive comparative analysis across multiple learning paradigms. The engineered feature space was processed by nine classical machine learning algorithms, eight deep neural network architectures, and four meta‐learning strategies. Empirical validation revealed that Logistic Regression achieved 88.57% accuracy with an AUC of 0.77. Further Extra Trees achieving 86.42% accuracy. Long Short‐Term Memory (LSTM) networks demonstrated strong sequential modeling capability, attaining a competitive accuracy of approximately 87%. While meta‐learning preserved diagnostic sensitivity, it did not surpass the leading standalone models. To ensure clinical interpretability, Explainability was employed, identifying delta and theta band power as the most critical discriminative features. Consequently, this study delivers a technically rigorous, transparent, and automated pipeline for electrophysiological signal characterization, establishing a preliminary computational foundation for neurophysiological differentiation in pediatric ASD. All findings are preliminary and exploratory, pending validation in larger independent cohorts.
Kazi Mahatir Mohammad Samir, A. Sarker, Sawrav Das· The FASEB Journal· 0 citations
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition whose early identification remains challenging because clinical assessment still relies heavily on behavioral observation and expert judgment. Electroencephalography (EEG) offers a noninvasive approach for capturing neural dynamics. Still, EEG-based ASD classification remains difficult because of signal nonstationarity, limited sample sizes, and the risk of subject-identity leakage. This study proposes a comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal convolutional network with self-attention (TCN-SA). Resting-state eyes-open EEG signals were acquired from only 10 participants, consisting of 5 individuals with ASD and 5 typically developing controls, using a 16-channel acquisition system. The signals were filtered using a fourth-order Butterworth band-pass filter, transformed into PCA or MS-PCA representations, segmented into 4 s windows with 50% overlap, and evaluated using subject-wise 5-fold cross-validation to reduce subject-identity leakage. The results showed that MS-PCA produced higher descriptive performance than PCA in both temporal architectures, with the strongest descriptive result obtained by the MS-PCA + TCN-SA scheme, which achieved a mean accuracy of 97.96 ± 2.37% and balanced precision, recall, F1-score, and specificity. However, the inferential comparison between PCA and MS-PCA did not reach statistical significance at the 0.05 level, and the cohort size was limited to 10 participants. Therefore, these findings should be interpreted as preliminary descriptive evidence within the present cohort rather than evidence of diagnostic readiness or robust clinical applicability. Larger, independent, and demographically diverse EEG datasets with richer clinical characterization are required to confirm the observed trend and evaluate the generalizability of the proposed framework.
M. Muliyadi, Melinda, Yuwaldi Away et al.· Journal of Electronics Elect...· 0 citations
Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition in children. Electroencephalography (EEG) may support objective assessment, but subject-independent ADHD classification remains challenging because EEG varies across participants and recording segments. We developed the EEG Multiscale Convolutional Network (EEG-MSCNet), a multiscale convolutional architecture for pediatric EEG classification, and evaluated it using subject-level leave-one-subject-out (LOSO) validation on a public EEG dataset. The study also examined preprocessing choices, architectural ablations, channel-region subsets, computational complexity, and repeated-run stability. The final EEG-MSCNet evaluation achieved an Accuracy of 87.6%, F1-score of 87.6%, and area under the receiver operating characteristic curve (AUROC) of 88.2%. Artifact subspace reconstruction (ASR) combined with Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER) emerged as the strongest preprocessing configuration. The multiscale architecture showed stronger observed Accuracy and F1-score than reduced architectural variants, and the full montage provided the strongest channel configuration. These findings show that subject-level validation, preprocessing, and architectural decisions materially influence EEG-based ADHD classification. EEG-MSCNet contributes both a model and a systematic evaluation framework, supported by source code, configurations, logs, and the public dataset reference for reproducible comparison.
Javier Sanchis, T. Kechadi, Miguel A. Teruel et al.· Multidimensional systems and...· 0 citations
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.
Madhuparna Das, Poulomi Pal, M. Mahadevappa· IEEE Transactions on Instrum...· 0 citations
Early and objective screening of Autism Spectrum Disorder (ASD) remains challenging because conventional diagnosis primarily relies on behavioural assessment and clinical observation. To address this limitation, this study proposes a dual-domain computational framework for automated EEG-based ASD classification by integrating complementary time-frequency analysis with Horizontal Visibility Graph (HVG)-based network modelling. Four time-frequency decomposition techniques, namely the Short-Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT), Wigner-Ville Distribution (WVD), and Superlet Transform (SLT), were employed to characterise the non-stationary dynamics of resting-state EEG signals. The resulting time-frequency representations were transformed into HVG networks, from which 17 graph-theoretic descriptors were extracted and evaluated using conventional machine learning classifiers, including a Soft Voting Ensemble. Among the investigated methods, the DWT-HVG framework combined with the Soft Voting Ensemble achieved the best performance, yielding an accuracy of 93.54%, sensitivity of 94.32%, specificity of 92.76%, F1-score of 93.62%, balanced accuracy of 93.54%, and an Area Under the Curve (AUC) of 98.17% using stratified 10-fold cross-validation. Statistical analysis using the Wilcoxon signed-rank test further confirmed the superiority of the DWT-based representation over the STFT, WVD, and SLT-based approaches. These findings demonstrate that integrating multiresolution time-frequency analysis with HVG-based graph-theoretic feature extraction provides an accurate, interpretable, and computationally efficient framework for objective EEG-based ASD screening.
Using a portable EEG device with a low participant burden and a deep learning model to distinguish between the typical development group (TD) and the NDD group, comprising children with ASD, ADHD, and ASD + ADHD is used.