This paper proposes a new hybrid model for classifying stress states using EEG signals, combining multi-domain transfer entropy (TrEn) with a two-dimensional PCANet (2D-PCANet) approach. The aim is to create an automated system for identifying stress levels, which is crucial for early intervention and mental health management. A major challenge in this field lies in extracting meaningful emotional information from the complex patterns observed in EEG. Our model addresses this by initially applying independent component analysis (ICA) to purify the EEG signals, enhancing the clarity for further analysis. We then leverage the adaptability of the fractional Fourier transform (FrFT) to represent the EEG data in time, frequency, and time–frequency domains. This multi-domain representation allows for a more nuanced understanding of the brain’s activity in response to stress. The subsequent stage involves the deployment of a two-layer 2D-PCANet network designed to autonomously distill EEG features associated with stress. These features are then classified by a support vector machine (SVM) to determine the stress state. Moreover, stress induction and data acquisition experiments are designed. We employed two distinct tasks known to trigger stress responses. Other stress-inducing elements that enhance the stress response were included in the experimental design, such as time limits and performance feedback. The EEG data collected from 15 participants were retained. The proposed algorithm achieves an average accuracy of over 92% on this self-collected dataset, enabling stress state detection under different task-induced conditions.

A lighting imaging system for dry eye testers is designed for the Placido disc features. Among them, the illumination light source adopts the flexible circuit board annular 850 nm LED light source, light through the Placido disc that can make the eye table brightness and illumination uniformity, it is conducive to obtain a clear eye plaque Placido annular image using the image acquisition system. At the same time, an imaging objective lens with a working distance of 100 mm is designed, and the objective lens has a MTF value of more than 0.3 at a spatial frequency of 135 lp/mm in the full field of view. It corrected various aberrations well and obtained excellent photo quality. The results show that this design can clearly display the annular Placido disc images on the tear film.

Sleep is crucial for optimal sensorimotor integration, a fundamental process enabling coordinated motor responses to sensory inputs. However, the neurophysiological mechanisms through which acute sleep deprivation impairs this integration remain incompletely understood. This study investigates the impact of acute sleep deprivation on postural balance and elucidates the underlying multilayered mechanisms using a combination of behavioral, psychophysiological, and neuroimaging indicators. Twenty-five healthy young participants underwent 36 hours of total sleep deprivation. Before and after the deprivation period, data were collected on postural stability metrics, psychomotor vigilance (PVT), critical flicker fusion frequency (CFF), resting-state electroencephalography (EEG), and resting-state functional magnetic resonance imaging (fMRI). Correlation analyses were performed to examine the associations between changes in behavioral performance (postural balance, PVT, and CFF) and alterations in psychophysiological measures (EEG spectral power and fMRI resting-state activity). Sleep deprivation significantly impaired balance, particularly with eyes closed, and was associated with reduced alertness and increased visual fatigue. EEG revealed elevated low-frequency power in occipital and frontal regions. fMRI showed altered activity in sensorimotor-related areas, especially the caudate nucleus, cerebellum, and thalamus. Acute sleep deprivation impairs postural stability by disrupting key nodes and networks involved in sensorimotor integration. This disruption manifests as reduced visual cortical excitability (affecting sensory input), weakened cognitive regulation within the frontoparietal network (impairing sensory processing and motor planning), and altered functional status of subcortical sensorimotor hubs (compromising motor coordination and feedback). These findings demonstrate that sleep deprivation compromises the neural circuitry governing the transformation of sensory information into appropriate motor outputs for balance control. This study provides comprehensive multimodal neuroimaging evidence for the neurobiological mechanisms linking insufficient sleep to impaired sensorimotor function.