The potentiostat is critical in the development of electrochemical systems; however, its cumbersome detection and high cost considerably limit its large-scale application. To provide an affordable alternative to developing countries and resource-constrained areas, this study designs an electrochemical detection system based on smartphones, which uses Bluetooth Low Energy to convert open-source potentiostat data based on PSoC-5LP. The WeChat application on the smartphone provides an interface for entering experimental parameters and visualizing the results in real time. The smartphone-based electro-chemical detection system has a simple design and reduces the size (10?3?0.3 cm) and the cost of the hardware ($ 18). The system performs the most commonly used cyclic voltammetry for electrochemical detection, with results that are comparable to those obtained using a commercial potentiostat and an error rate of 1.3 %. In the classical teaching experiment of electrochemical determination of ascorbic acid in orange juice samples, the measured value of the system is 0.367 ? 0.012 mg/mL, compared with the standard reference value of 0.37 mg/ mL, which is obviously a convincing value. Therefore, this system is a low-cost, reliable alternative to a potentiostat for research, education, or product integration development.
The use of brain–computer interfaces (BCIs) to control intelligent devices is a current and future research direction. However, the challenges of low accuracy of real-time recognition and the need for multiple electroencephalographic channels are yet to be overcome. While a number of research teams have proposed many ways to improve offline classification accuracy, the potential problems in real-time experiments are often overlooked. In this study, we proposed a label-based channel diversion preprocessing to solve the problem of low real-time classification accuracy. The Tikhonov regularised common spatial-pattern algorithm (TRCSP) and one vs rest support vector machine (OVR-SVM) were used for feature extraction and pattern classification. High accuracy was achieved in real-time three-class classification using only three channels (average real-time accuracy of 87.46%, with a maximum of 90.33%). In addition, the stability and reliability of the system were verified through lighting control experiments in a real environment. Using the autonomy of MI and real-time feedback of light brightness, we have built a fully autonomous interactive system. The improvement in the real-time classification accuracy in this study is of great significance to the industrialisation of BCI.
Restoring the motor function of paralyzed limbs has always been an important aim in the field of biomedical engineering. In view of the discovery of the repeatable experimental phenomenon that positive and negative stimulation pulses applied to specific points, identified as central pattern generator (CPG) sites, could induce switching of the movement patterns of bilateral hindlimbs, an improved Hodgkin–Huxley (HH) neuron model was established by introducing the electric field effect principle. A CPG neural network model comprising 12 neural units in six joints of the bilateral hindlimbs was modeled. The simulation results showed the alternating movement patterns of the bilateral hindlimbs through the action potential release of extensor and flexor neurons. The explosive electromyogram of the gastrocnemius (GM) and quadriceps femoris (QF) when stimulating the CPG sites with intraspinal micro-stimulation (ISMS) was consistent with the action potential diagram of the flexor and extensor neurons obtained via simulation. Our research considers the neural network model of electric field radiation, which can facilitate a deep understanding of the dynamic characteristics of neurons in the electric field environment, and verifies the correlation between the location of CPG sites, stimulus polarity and movement patterns to induce alternating left–right coordinated movements.
A spinal stimulator that can regulate hindlimb movements using monopolar stimulation has not been developed yet. Nevertheless, in a previous study, we found a specific central pattern generator site on the right side of the rat spinal cord. By stimulating these sites with certain pulse signals, the alternating movement of the hindlimb can be obtained using fewer electrodes. Therefore, in this research, considering the specific central pattern generator site as the target, functional electrical stimulation was performed on rats with spinal cord injury using monopolar stimulation. Angle sensors were used to track and capture the knee joint angle data of the right hindlimb; thus, the mapping relationship between the voltage amplitude and the knee angle parameters was established. Based on this relationship, the rats’ hindlimb were controlled. Compared with the traditional spinal stimulator, the proposed approach increases the gait feedback, requires fewer electrodes, and simplifies the timing of stimulation. The rats with spinal cord injury were subjected to stimulation training for half an hour every day for 28 consecutive days. The Basso, Beattie and Bresnahan score showed that 76% of the health level could be achieved on the 28th day. Finally, somatosensory evoked potential analysis showed that the measurement results were close to the standard value on the 28th day. This study lays a foundation for future rehabilitation research on the hindlimb.
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