DynaSPARC: a dynamic single-trial P300 paradigm for assessing motor response capability in autism spectrum disorder.

Abstract

Objective.EEG-based assessment of motor response capability is critical for understanding the heterogeneous cognitive profiles of children with autism spectrum disorder (ASD), e.g. typically P300 event assessment. However, mainstream methods generally struggle to address the entangled trial-to-trial variability and temporal instability of P300 signatures in ASD, fixed time windows or trial-averaging primarily effective only for typically developing subjects.Approach.To overcome this limitation, this paper introducesDynaSPARC(DynamicSingle-trialP300Assessment ofResponseCapability), a framework based on the premise that behavioral reaction time () provides a latent regulation for inferring trial-specific cognitive dynamics.DynaSPARCenables reliable assessment of motor response capability in ASD through: (1)Dynamic Temporal Windowing, which uses a nonlinear mapping of standardizeddifferences to adaptively parameterize the start and length of the P300 window for each trial within a physiologically constrained range (e.g. 300-800 ms); (2)Collaborative Spatio-temporal Attention, which employs a learnable temporal filter to pinpoint P300 latency and a knowledge-guided channel weighting scheme for a reliable reconstruction of the P300 signature.Main results.Evaluation on an EEG dataset from 67 children (29 with ASD vs 38 typically developing) through a motor-cognitive task demonstrates thatDynaSPARCachieves superior performance over fixed-window methods: (1) A more temporally localized and spatially plausible single-trial P300 was obtained, and a substantially stronger-peak-latency coupling was preserved (Spearman= 0.61,= 1.33vs= 0.11, p = 0.391), consistent with more stable peak-latency estimates (reduced jitter); (2) The peak scalp topography showed a more canonical, spatially focused centro-parietal positivity; (3) The classification accuracy,F1score, recall, and precision reached 84.77%, 78.43%, 84.03%, and 76.14%, respectively.Significance.This work establishes a new paradigm for EEG-based assessment, moving beyond static averaging to model the dynamic interplay between neural latency and behavioral output.