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. 2026 Jul 8;17:1871218. doi: 10.3389/fphys.2026.1871218

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Copyright © 2026 Hu, Liu, Jiang, Wang, Qian, Ren and Kui.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Biplot chart illustrating principal component analysis with PC1 labeled as Passive Deprivation Component (43.2%) on the x-axis and PC2 as Active Overload Component (34.1%) on the y-axis; red arrows represent Engagement Index, LF/HF Ratio, Pupil Diameter, and Beta Power, while blue arrows represent Blink Rate, Fatigue Index, RMSSD, and Alpha Power, each pointing in distinct directions from the origin. — Principal Component Analysis (PCA) loading plot of the multimodal physiological features, cumulatively explaining 77.3% of the total variance. Variables are visually grouped along two exploratory components: the passive-deprivation-related indicators (RMSSD, Alpha power, FI, and blink rate) and the active-overload-related indicators (LF/HF, Beta power, EI, and pupil diameter). PCA was interpreted as exploratory dimensionality reduction rather than confirmatory validation of latent mechanisms.