Study on exploring the relationships between physiological indicators in near-death experiences by drawing on in-mold electronics and node displacement concepts in brain-computer interface signal transmission.

内容摘要

The association between near-death experiences (NDEs) and physiological indicators remains an unsolved mystery, which hinders in-depth understanding of the essence of consciousness and life processes. Traditional single-indicator analysis methods have limitations, and a comprehensive multi-modal research approach is urgently needed to advance relevant explorations in the fields of medicine, neuroscience, and philosophy. Multi-modal physiological monitoring data, including electroencephalogram (EEG) and electrocardiogram (ECG), from critical care settings (ICU, emergency department) were integrated. Signal analysis was conducted by drawing analogies from the concepts of in-mold electronics (IME) and injection molding node displacement. Latin Hypercube Sampling (LHS) was used to collect injection molding parameters, and the Multi-Strategy Differential Evolution (MSDE) algorithm (incorporating elite-sharing, perturbation-backtracking, and adaptive-tuning strategies) was combined to optimize the injection molding process of the brain-computer interface (BCI). A node displacement prediction model was constructed through Moldex3D simulation and Kriging interpolation. During the out-of-body sensation phase of NDEs, EEG showed an increase in gamma waves and a decrease in alpha waves, while ECG exhibited arrhythmia, confirming the coordinated changes between the brain and the heart. In BCI manufacturing, the MSDE algorithm reduced the average node displacement from 0.289 mm to 0.021 mm (with an optimization rate of 92.73%), the volume shrinkage rate from 10.162% to 6.39%, and the optimized voltage difference from 5.78 V to 0.42 V, which was consistent with the improvement in displacement. Multi-dimensional analysis is crucial for decoding the mechanism of NDEs. The optimized BCI hardware enables accurate collection of NDE-related physiological signals, providing scientific support for end-of-life care, optimization of resuscitation protocols, and consciousness research, while also building a cross-disciplinary bridge between engineering and life sciences.