Multimodal Physiological Responses to Graded Cognitive Workload: A Randomized Crossover Human-Subjects Experiment (Preregistration)

This study intends to collect multimodal data including EEG, EMG, EDA, eye movement, HRV/ECG and behavioral performance synchronously within single subjects through multi-attribute behavioral tasks with three difficulty gradients (low, medium and high; L1/L2/L3). It analyzes the response patterns of physiological and psychological indicators under different workload levels and constructs a quantitative workload assessment model based on multimodal signals. Stratified (by age) permuted block randomization with a block size of 6 is adopted, and sequentially numbered, opaque, sealed envelopes (SNOSE) are used to achieve allocation concealment. Six order groups (OG-1…OG-6) are assigned the sequence of six trial conditions determined by mirrored replica permutation. The purpose of this research is mechanism exploration and model construction, and it is non-therapeutic and non-clinical. This deposit provides the pre-registration evidence that the randomization scheme and experimental protocol for the study 'Multi-modal physiological response characteristics under graded cognitive workload' were fixed prior to any subject enrollment. It includes: (1) the pre-registration record documenting the stratified permuted block randomization design (age strata S1/S2, block size 6, six order groups with mirror-replica permutations), allocation concealment via SNOSE sealed envelopes, and the cryptographic commitment; (2) the allocation master table (allocation_master.csv, 60 slots); (3) the reproducible generation/verification script (allocate.py); (4) metadata with SHA-256 hashes and the random seed (1654857602); (5) the verification report confirming all constraints PASS. The task program parameters (dot/number sequences, gauge drift, tracking signal) are fixed by the program and calibrated in a pilot study, shared across all participants, and not randomized. SHA-256: allocation_master.csv = 0338eb63bac83147ea9437874d3207d6281594d6dac099111ce20a6e4212d819; allocate.py = dd1e28ee8a0e5774a1db04db6ccd78daadb24b47ab62fee7d9d2de7a0baa1ac2. Generated at 2026-08-12T11:22:36+08:00.