A continuous flight task load can induce fatigue and lead to changes in electroencephalography (EEG). EEG microstates can reflect the activities of large-scale neural networks during mental fatigue. This exploratory experiment explored the effects of mental fatigue induced by continuous simulated flight multitasking on EEG microstate indices. Twenty-four participants performed continuous 2-hour aircraft piloting simulation while EEG were recorded. The Stanford sleepiness scale (SSS) and critical flicker fusion frequency (CFF) were measured before and after the task. Microstate analysis was applied to EEG. Four microstate classes (A-D) were identified during the pre-task, post-task, beginning, and end phases. The effects of mental fatigue were analyzed. Compared with the pre-task, the post-task had a higher global explained variance (GEV) and time parameters of class C but lower occurrence and coverage of class D. The end had a higher GEV but lower duration and coverage of class D than at the beginning. After 2 h of multitasking, the transition probability between A and D, and between B and D decreased but between A and C increased. Subjective fatigue scores were negatively correlated with occurrence and coverage of class D. Task performance was negatively correlated with duration and coverage of class C but positively correlated with duration and occurrence of class B. Time parameters and transition probability of EEG microstates can detect mental fatigue induced by continuous aircraft piloting simulation. The global brain network activation of mental fatigue can be detected by EEG microstates that can evaluate flight fatigue.

Individualized lung-protective ventilation strategy is essential for patients with COVID-19. In this study, a wireless, low-power, and miniaturized electrical impedance tomography (EIT) system was developed for remote and long-term monitoring of lung ventilation for patients with COVID-19 in the isolation ward of ICU. A new strategy of combining filtering and improved voltage-controlled current was employed to design the current source for a simplified system structure, and a new differential receiver circuit consisting of a buffer amplification and a differential amplification was proposed to design the voltage measurement unit for high precision. Moreover, a Bluetooth interface was adopted for wireless data transmission, and components characterized by low power consumption were selected to minimize the system power consumption. The proposed EIT system occupies a total size of $6.5\times 4.5\times0.6$ cm ³ and can work stably within 25 m from the wireless terminal installed with host EIT software. The system has a signal-to-noise of 70 dB at 50 kHz, stability of 0.1% relative change, and power consumption of 114 mW, as tested with a resistor phantom. Furthermore, a comparison between the proposed system and a commercial ICU EIT device was conducted on nine healthy volunteers for ventilation monitoring. The correlation between lung volume and relative impedance changes was higher than 0.9 for both EIT systems ( $p < 0.001$ ). The analysis of the corresponding EIT images revealed that both systems delivered comparable images in terms of linearity, repeatability, and regional ventilation distribution. The test and experimental results suggested that the proposed system could conveniently provide reliable data acquisition (DAQ) for remote and long-term lung ventilation monitoring.

Liver is an important parenchyma organ, and its tissue viability plays an important role in liver transplantation and liver ischemic injury assessment. Dielectric property is a useful biophysical feature that provides insights into the structure and composition of biological tissues. This work aims to establish the relationship between the dielectric properties and viability of human normal hepatic tissues and explore the possibility of evaluating tissue viability by using dielectric properties. First, data on dielectric properties and tissue viability (including cell morphology and enzyme indicators) were collected from human liver tissues at 0.25-24 h after isolation. Grey relational analysis was conducted to select dielectric property and tissue viability indices that were highly correlated with prolonged ex vivo time as the inputs and outputs, respectively, of back-propagation (BP) neural network analysis. Finally, a BP neural network was developed with the Levenberg-Marquardt algorithm to explore the possibility of using dielectric properties as the basis for tissue viability evaluation. Results showed that the mean relative error for prediction was 2.40%, indicating that the model showed potential in forecasting liver tissue viability by applying dielectric properties.

Transcutaneous vagal nerve stimulation (tVNS) is a non-invasive nerve stimulation technique that exerts a positive “exogenous” online neuromodulatory effect on inhibitory control (IC). Additionally, IC training (ICT) is an effective approach for enhancing IC via the “endogenous” activation of brain regions implicated in this process. The aim of the present study was to examine the synergistic effects of tVNS and ICT on IC enhancement. For this, we measured the changes in neural activity in frontal, fronto-central, and central regions in the time domain of the N2 component and the frequency domain of alpha power during the stop signal task. A total of 58 participants were randomly divided into four groups that received five sessions of either ICT or sham ICT with either online tVNS or sham tVNS. No differences in N2 amplitude were detected after any of the interventions. However, N2 latency shortened after tVNS + ICT in frontal, fronto-central, and central regions. N2 latency shortened after the intervention of sham tVNS + ICT in frontal region. Moreover, alpha power after tVNS + ICT intervention was larger than those of the other interventions in frontal, fronto-central, and central regions. The obtained electrophysiological data suggested that combining tVNS with ICT has synergistic ameliorative effects on IC, and provide evidence supporting the IC-enhancing potential of tVNS combined with ICT.