As a stress hormone existing in the human body, cortisol can reflect the psychological stress and health status in daily life, and is a potential biomarker of the body’s stress response. To effectively collect sweat and accurately identify the target, this paper reports a flexible wearable cortisol detection device with outstanding reliability and sensitivity. Molecular imprinted polymer (MIP) ensures cortisol specificity. And carbon nanotubes (CNT) on electrodes increase sensitivity, expanding the detection range to 10⁻³ to 10⁴ nM, with sensitivity at 189.2 nA/lg(nM). In addition, porous chitosan hydrogel (PCSH) collects sweat effectively, its adhesive properties and 80% swelling rate offer a low-cost alternative to microfluidics. Flexible printed circuit board (FPCB) and serpentine electrode (SE) ensure device durability. This non-invasive, highly sensitive device offers a novel method for mental stress monitoring and clinical diagnosis, advancing human physiological state monitoring.

Activated channels of functional near-infrared spectroscopy are typically identified using the desired hemodynamic response function (dHRF) generated by a trial period. However, this approach is not possible for an unknown trial period. In this paper, an innovative method not using the dHRF is proposed, which extracts fluctuating signals during the resting state using maximal overlap discrete wavelet transform, identifies low-frequency wavelets corresponding to physiological noise, trains them using long-short term memory networks, and predicts/subtracts them during the task session. The motivation for prediction is to maintain the phase information of physiological noise at the start time of a task, which is possible because the signal is extended from the resting state to the task session. This technique decomposes the resting state data into nine wavelets and uses the fifth to ninth wavelets for learning and prediction. In the eighth wavelet, the prediction error difference between the with and without dHRF from the 15-s prediction window appeared to be the largest. Considering the difficulty in removing physiological noise when the activation period is near the physiological noise, the proposed method can be an alternative solution when the conventional method is not applicable. In passive brain-computer interfaces, estimating the brain signal starting time is necessary.

Background/Objectives: Multimodal neuroimaging, particularly the integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has emerged as a key methodology for investigating brain function and classifying neural activity. However, the efficient fusion of these two signals remains a formidable challenge due to their significant spatio-temporal heterogeneity. This paper presents the BiSTF-Net, which integrates decoupled and bi-directional spatio-temporal fusion mechanisms to enhance the performance of cognitive task recognition. Methods: In BiSTF-Net, the spatial features of EEG and fNIRS are mutually guided and enhanced through an efficient bi-directional cross modal guidance (Bi-CMG). Then, the temporal latencies of fNIRS signals are aligned in a data-driven manner using adaptive temporal alignment (ATA). Subsequently, the aligned features are deeply fused into a modality-invariant, discriminative representation via a symmetric cross-attention fusion (SCAF) module. Results: Evaluated on the mental arithmetic (MA), motor imagery (MI), and word generation (WG) tasks, the BiSTF-Net achieves average accuracies of 83.33%, 82.09%, and 84.99% respectively. Conclusions: The BiSTF-Net exhibits superior performance compared to the existing methods, offers a robust and interpretable solution for multimodal EEG-fNIRS cognitive task classification, and provides a methodological foundation for future extensions to other multimodal data and broader real-world clinical applications.

Strong thermo-optic effect of polymers is useful for fabricating tunable phase-delay devices with low power consumption and wide tuning range. However, if the temperature change is increased to extend the tuning range, an attenuation of the guided light is accompanied by the refractive index gradient across the waveguide core. For three different waveguide structures, in this study, the optimal structure of the device for a variable phase delay line was found by investigating the attenuation and interference phenomena accompanying phase modulation. By improving the uniformity of thermal distribution across the waveguide core, a phase delay of 130π rad was obtained with an output attenuation less than 0.5 dB.

To meet diverse industrial needs, temperature sensors with a wide measurement range have become a key element. In this paper, we propose an asymmetric Mach–Zehnder interferometer (MZI) temperature sensor based on polymer optical waveguides. Experimental results show that the output interference signal exhibits periodic changes with temperature variations. The device exhibits a temperature measurement range of 120 °C and a sensitivity of 0.27 rad/°C. This study provides an effective new approach for developing high-performance, low-cost temperature sensors suitable for an extended temperature measurement range.