Quantitative EEG-based Autism Spectrum Disorder Detection Using Neural Sequence Models

Background: Autism Spectrum Disorder (ASD) affects approximately 1% of the global child population, yet current gold-standard diagnostic methods remain time-intensive and expertisedependent. Electroencephalography (EEG) offers an objective and scalable approach for neurophysiological measurement, facilitating early detection. Methods: This study evaluated three neural sequence architectures -Long Short-Term Memory (LSTM), Transformer, and Mamba (Selective State Space Model) -for ASD classification using 47-channel, 150-second resting-state EEG recordings from 56 adults (28 with ASD, 28 controls) from the University of Sheffield dataset. Data were preprocessed using MNE-Python with bandpass filtering (0.50-50 Hz), Independent Component Analysis (ICA) artifact removal, and zscore normalization. Models were trained on epochs of varying durations (1 s, 2.50 s, 5 s) using stratified 5-fold cross-validation, with performance evaluated on a held-out test set (15%). Mixture-of-Experts (MoE) ensembles were constructed using performance-based weighted averaging. Regional classification and spectral analyses identified anatomical and frequency-specific biomarkers. Results: The Mamba model achieved 98.18% accuracy with only 2972 parameters and a training time of 0.09 min at 2.50-second epochs. LSTM (144,578 parameters) reached 95.25% accuracy, while Transformer (38,946 parameters) attained 94.41%. The optimal Mamba+LSTM ensemble achieved 98.46% accuracy (Cohen's kappa=0.97, ROC-AUC=99.84%) with only 11 misclassifications from 716 test samples. Regional analysis revealed frontal lobe dominance (76.81% accuracy, 25 channels) with theta-band (4-8 Hz) biomarkers. Spectral analysis confirmed characteristic ASD patterns: elevated delta/theta power, suppressed alpha rhythm, and increased beta/gamma activity. Single-channel analysis identified C5 (left central, 58.80% accuracy) as the most discriminative electrode. Conclusions: Neural sequence models, particularly the parameter-efficient Mamba architecture and the Mamba+LSTM ensemble, demonstrate exceptional performance for EEG-based ASD classification, offering a clinically scalable and objective diagnostic tool. The frontal-central electrode configuration and theta-band biomarkers provide neurophysiologically interpretable features suitable for portable EEG systems and early screening applications.