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Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12–40 Hz) power increased after the NFB training for both the elbow and the shoulder joints’ movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals.

Brain–Machine Interfaces (BMIs) have attracted much attention in recent decades, mainly for their applications involving severely disabled people. Recently, research has been directed at enhancing the ability of healthy people by connecting their brains to external devices. However, there are currently no successful research reports focused on robotic power augmentation using electroencephalography (EEG) signals for the shoulder joint. In this study, a method is proposed to estimate the shoulder’s electromyography (EMG) signals from EEG signals based on the concept of a virtual flexor–extensor muscle. In addition, the EMG signal of the deltoid muscle is used as the virtual EMG signal to establish the EMG estimation model and evaluate the experimental results. Thus, the shoulder’s power can be augmented by estimated virtual EMG signals for the people wearing an EMG-based power augmentation exoskeleton robot. The estimated EMG signal is expressed via a linear combination of the features of EEG signals extracted by Independent Component Analysis, Short-time Fourier Transform, and Principal Component Analysis. The proposed method was verified experimentally, and the average of the estimation correlation coefficient across different subjects was 0.78 (±0.037). These results demonstrate the feasibility and potential of using EEG signals to provide power augmentation through BMI technology.

A power-assisted exoskeleton should be capable of reducing the burden on the wearer’s body or rendering his or her work improved and efficient. More specifically, the exoskeleton should be easy to wear, be simple to use, and provide power assistance without hindering the wearer’s movement. Therefore, it is necessary to evaluate the backdrivability, range of motion, and power-assist capability of such an exoskeleton. This evaluation identifies the pros and cons of the exoskeleton, and it serves as the basis for its subsequent development. In this study, a lightweight upper-limb power-assisted exoskeleton with high backdrivability was developed. Moreover, a motion capture system was adopted to measure and analyze the workspace of the wearer’s upper limb after the exoskeleton was worn. The results were used to evaluate the exoskeleton’s ability to support the wearer’s movement. Furthermore, a small and compact three-axis force sensor was used for power assistance, and the effect of the power assistance was evaluated by means of measuring the wearer’s surface electromyography, force, and joint angle signals. Overall, the study showed that the exoskeleton could achieve power assistance and did not affect the wearer’s movements.

Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals’ daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, we aim to estimate the necessary torque information for a subject from his/her electroencephalography (EEG) signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external torque sensors nor electromyography (EMG) sensors. In this paper, we focus on extracting the motion-relevant EEG signals’ features of the shoulder joint, which is the most complex joint in the human’s body, to construct a power assistance system using wearable upper limb exoskeleton robots with BMI technology. We extract the characteristic EEG signals when the shoulder joint is doing flexion and extension movement freely which are the main motions of the shoulder joint needed to be assisted. Independent component analysis (ICA) is used to extract the source information of neural components, and then the average method is used to extract the characteristic signals that are fundamental to achieve the control. The proposed approach has been experimentally verified. The results show that EEG signals begin to increase at 300–400 ms before the motion and then decrease at the beginning of the generation of EMG signals, and the peaks appear at about one second after the motion. At the same time, we also confirmed the relationship between the change of EMG signals and the EEG signals on the time dimension, and these results also provide a theoretical basis for the delay parameter in the linear model which will be used to estimate the necessary torque information in future. Our results suggest that the estimation of torque information based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities continuously.

The increase in demand for the fast charging of electric vehicles (EVs) has promoted research and the application of high-power direct current (DC) fast charging stations, and research on the stability control of charging stations with a parallel structure of multi-controllable rectifier modules is of great significance. Taking into account the dynamic and steady-state performance, the parameters of the dual-loop controller based on virtual impedance control are designed to realize the power sharing of multi-controllable rectifier modules. The constant power load model of EV charging at a constant current is established, and the load capacity of the cascaded system of converters is studied by using the impedance analysis method. The experimental and simulation results verify the effectiveness and correctness of the power sharing control strategy and the cascaded system stability analysis.

Rhizobia inoculation is widely used in legumes to improve the yield and quality of forage. However, the symbiotic interactions of different legumes are specific. The search for efficient strains plays an important role in legume forage and in practical production. In this study, Desmodium intortum was first inoculated with rhizobia from different regions, and then the production traits, nutritional quality, and fermentation of D. intortum in the rumen were evaluated according to the rhizobia strains. The results showed that, compared with the control, inoculation with different rhizobia strains significantly increased the number of nodules, shoot fresh weight, crude protein content, plant protein yield, and ether extract content of D. intortum, and inoculation with the R3 strain (Bradyrhizobium elkanii) increased these values by 61.0%, 29.6%, 16.1%, 62.9%, and 30.4%, respectively. In the basal diet of Pennisetum purpureum Rich. × P. americana King grass cv. Reyan No.4, D. intortum inoculated with different rhizobia was added (75:25), and the combination of the inoculated R3 and R5 strains increased the rumen’s ammonia-nitrogen (NH3-N) levels and in vitro dry matter digestibility (IVDMD): R3 increased the contents of NH3-N and IVDMD by 50.1% and 12.3%, respectively, while R5 increased them by 50.9% and 15.4%, respectively. Based on a comprehensive evaluation through principal component analysis, R3 was ranked first overall. These results support the development of competitive inoculants from indigenous strains as a strategy to improve the nutritional value of D. intortum and ruminal fermentation.