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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. [ABSTRACT FROM AUTHOR]

Sleep deprivation impairs cognitive function and is accompanied by a simultaneous compensatory effect, one of the brain's capacities to maintain function in emergency situations. However, the time course of the compensatory effect is unclear. In this study, 22 male participants completed a pronunciation working memory task that included congruent and incongruent stimuli trials with EEG recordings before and after total sleep deprivation (TSD). Behavioral performance analysis showed that after TSD, the participants' reaction time (RT) was shortened, but accuracy was reduced significantly. Analysis of event-related potential (ERP) results showed that the amplitude of N2 (an early visual ERP) was larger (i.e. more negative) after TSD than at baseline. A significant interaction between congruency and sleep condition was seen. Compared to that before TSD, the increase in amplitude of P3 (a stimulus-induced positive deflection component) under an incongruent stimulus was larger than that under a congruent stimulus after TSD. Moreover, a significant negative correlation was found between P3 amplitude and RT. Our results suggest that TSD impairs cognitive function. Meanwhile, the brain activates a compensatory mechanism after TSD, which is comprehensive during the conflict-detection and information-updating stages. This study provides a fresh viewpoint for understanding how TSD affects cognitive function. [ABSTRACT FROM AUTHOR]

Total sleep deprivation (TSD) leads to cognitive decline; however, the neurophysiological mechanisms underlying resting-state electroencephalogram (EEG) changes after TSD remain unclear. In this study, 42 healthy adult participants were subjected to 36 h of sleep deprivation (36 h TSD), and resting-state EEG data were recorded at baseline, after 24 h of sleep deprivation (24 h TSD), and after 36 h TSD. The analysis of resting-state EEG at baseline, after 24 h TSD, and after 36 h TSD using source localization analysis, power spectrum analysis, and functional connectivity analysis revealed a decrease in alpha-band power and a significant increase in delta-band power after TSD and impaired functional connectivity in the default mode network, precuneus, and inferior parietal lobule. The cortical activities of the precuneus, inferior parietal lobule, and superior parietal lobule were significantly reduced, but no difference was found between the 24 h and 36 h TSD groups. This may indicate that TSD caused some damage to the participants, but this damage temporarily slowed during the 24 h to 36 h TSD period. [ABSTRACT FROM AUTHOR]

Purpose: The function of working memory (WM) is impaired by total sleep deprivation (TSD) and cognitive load. However, it is unclear whether the load modulates the effect of TSD on WM. We conducted a pilot study to investigate the effects of 36 h of TSD on WM under different load levels. Materials and methods: Twenty-two male students aged 18–25 years were enrolled, who underwent two types of sleep conditions (baseline and 36 h TSD), where they performed two N-back WM tasks (one-back task and two-back task) with simultaneous electroencephalography recordings. Results: Repeated measures analysis of variance (ANOVA) indicated that, with the increasing load, the reaction time increased and the accuracy decreased. After TSD, the correct number per unit time decreased. The significant interaction effect of the P3 amplitudes between the load level and the sleep condition showed that the reduction in the amplitude of P3 in the two-back task due to TSD was more obvious than that in the one-back task. Conclusions: Our results provided evidence for the moderation of load on the impairment of TSD on WM. The degree of TSD-induced impairment for a higher load was greater than that for a lower load. The current study provides new insights into the mechanisms by which sleep deprivation affects cognitive function. [ABSTRACT FROM AUTHOR]

Many studies have demonstrated the impairment of sustained attention due to total sleep deprivation (TSD). However, it remains unclear whether and how TSD affects the processing of visual selective attention. In the current study, 24 volunteers performed a visual search task before and after TSD over a period of 36 h while undergoing spontaneous electroencephalography. Paired-sample t-tests of behavioral performance revealed that, compared with baseline values, the participants showed lower accuracy and higher variance in response time in visual search tasks performed after TSD. Analysis of the event-related potentials (ERPs) showed that the mean amplitude of the N2-posterior-contralateral (N2pc) difference wave after TSD was less negative than that at baseline and the mean amplitude of P3 after TSD was more positive than that at baseline. Our findings suggest that TSD significantly attenuates attentional direction/orientation processing and triggers a compensatory effect in the parietal brain to partially offset the impairments. These findings provide new evidence and improve our understanding of the effects of sleep loss. [ABSTRACT FROM AUTHOR]

Summary: Sleep loss with work overload can impact human cognitive performance. However, the brain's response to an increased working memory load following total sleep deprivation (TSD) remains unclear. In the present study, we focussed on the dynamic response of the hippocampus to increased working memory load before and after total sleep deprivation of 36 h. A total of 16 male participants completed a verbal working memory task under functional magnetic resonance imaging. After whole‐brain activation analysis and region of interest analysis of the hippocampus, the generalised form of context‐dependent psychophysiological interactions (gPPI) was used to analyse the hippocampal functional connectivity with the whole brain. The results revealed that as the working memory load increased within a small range, from 0‐back to 1‐back task, the left hippocampal functional connectivity decreased with the left supplementary motor area, left pars opercularis, left rolandic operculum, right superior frontal gyrus, bilateral precentral gyrus, and left middle cingulate cortex following total sleep deprivation compared with that observed in resting wakefulness. When the working memory load further increased from 1‐back to 2‐back task, the connectivity increased between the left hippocampus and the left superior parietal lobule as well as between the left hippocampus and right lingual gyrus after total sleep deprivation compared with that observed in resting wakefulness. Moreover, the left hippocampus gPPI effect on the left middle cingulate cortex and left superior parietal lobule could predict the behavioural test accuracy in 1‐back and 2‐back task, respectively, following total sleep deprivation. These findings indicated that increased working memory load after total sleep deprivation disrupts working memory processes. The brain reacts to these disruptions in a dynamic and flexible manner, involving not only brain activation but also hippocampus‐related functional network connections. [ABSTRACT FROM AUTHOR]