No abstract

Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement. INDEX TERMS Quadcopter control system, motor imagery, steady-state visual evoked potential, off-line optimization, human-machine interaction.

Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after

BackgroundThe prognosis of severe COVID-19 patients is poor. Traditional Chinese Medicine had an advantage in keeping microenvironmental balance in treating SARS and COVID-19.MethodsThis prospective cohort study compared the efficacy and safety of integrative Chinese-Western medicine (ICWM) treatments with Western medicine (WM) treatments in severe or critically ill patients. The outcomes included: mortality, hospital stay in ICU, days with ventilator-assisted ventilation, etc.ResultsA total of 72 confirmed COVID-19 patients in ICU were included. The median age of patients was 66 years (IQR: 53-77.5), and there were 32 female patients (44.4%). There were no significant differences in laboratory tests and complications after treatments between groups. A total of 36 (50%) patients died during hospitalization, and the mortality in the ICWM group (28.6%) was significantly lower than that of the WM group (63.6%, adjusted P=0.011). And the time of assisted ventilation was shorter in the ICWM group (adjusted P=0.341). However, the median hospital stay was significantly longer in the ICWM group (18 vs. 14 days, adjusted P＜0.05).ConclusionsICWM treatments could significantly reduce mortality for severe or critically ill patients with COVID-19, and it was safe and cost-effective to add Chinese medicine.

Macular edema has three types of lesions: REA, PED and SRF. Early detection of edema areas can play a key role in the treatment of diseases. Neural network is a powerful tool for image processing in medical field. Deep learning automatically finds features that are ideal for “AI+Medical Imaging” diagnostics. This paper mainly proposes a new method of neural network that includes Object recognition and classification to improve the accuracy and speed of detection of macular edema area. The method is offered to be evaluated and compared to the traditional network method. The results indicate that the method applied reducing the over segmentation effect and getting a more accurate result of the option than traditional network method