Bridging Deep Learning and Quantum Computing for EEG-Driven P300 Targeted ASD Response Detection

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose diagnosis relies heavily on subjective behavioral assessments. Electroencephalography (EEG) provides an inexpensive and non-invasive method of objective biomarker detection, but is plagued by high dimensionality and noise that complicates automated analysis. This article introduces a hybrid Transformer-Quantum Neural Network (QNN) model that combines deep learning and quantum computing to enhance P300 targeted ASD responses from non-targeted responses. The model uses transformer capabilities to capture cross-domain correlations and feature-level dependencies across heterogeneous feature sources, and the quantum heads introduced via parameterized quantum circuits detect high-order, nonlinear relations in a Hilbert space via entanglement and superposition. Experimental results on the BCIAUT-P300 dataset better pure classical alternatives with higher diagnostic accuracy at higher accuracy (0.921) and F1-score (0.915). Statistical comparisons confirm that the hybrid approach yields significant improvements in discriminative reliability and diagnostic odds ratio, justifying quantum feature embedding's expressivity and efficiency. The results bear witness to the feasibility and potential of hybrid quantum-classical systems as potential future intelligent diagnostic devices for ASD responses from EEG signals.