Median nerve electrical stimulation is a common peripheral nerve electrical stimulation treatment technology in clinic. With simple operation, it has been widely used in clinical to promote coma after craniocerebral trauma, relieve pain, improve cognition, Parkinson’s disease and so on. However, its mechanism has always been a hot topic and difficult part. At present, there are a large number of clinical efficacy studies and animal experiments of median nerve electrical stimulation at home and abroad. This article reviews the clinical application and animal experiments of median nerve electrical stimulation in recent years, and summarizes its mechanism, hoping to contribute to relevant clinical applications and research.
Currently, commercial devices for electrical neural stimulations can only provide fixed stimulation paradigms with preset constant parameters, while the development of new stimulation paradigms with time-varying parameters has emerged as one of the important research directions for expanding clinical applications. To facilitate the performance of electrical stimulation paradigms with time-varying parameters in animal experiments, the present study developed a well-integrated stimulation system to output various pulse sequences by designing a LabVIEW software to control a general data acquisition card and an electrical stimulus isolator. The system was able to generate pulse sequences with inter-pulse-intervals (IPI) randomly varying in real time with specific distributions such as uniform distribution, normal distribution, gamma distribution and Poisson distribution. It was also able to generate pulse sequences with arbitrary time-varying IPIs. In addition, the pulse parameters, including pulse amplitude, pulse width, interphase delay of biphasic pulse and duration of pulse sequence, were adjustable. The results of performance tests of the stimulation system showed that the errors of the parameters of pulse sequences output by the system were all less than 1%. By utilizing the stimulation system, pulse sequences with IPI randomly varying in the range of 5~10 ms were generated and applied in rat hippocampal regions for animal experiments. The experimental results showed that, even with a same mean pulse frequency of ~130 Hz, for neuronal populations, the excitatory effect of stimulations with randomly varying IPIs was significantly greater than the effect of stimulations with fixed IPIs. In conclusion, the stimulation system designed here may provide a useful tool for the researches and the development of new paradigms of neural electrical stimulations.
Objective To review researches of treatment of peripheral nerve injury with neuromuscular electrical stimulation (NMES) regarding mechanism, parameters, and cl inical appl ication at home and abroad. Methods The latest original l iterature concerning treatment of peri pheral nerve injury with NMES was extensively reviewed. Results NMES should be used under individual parameters and proper mode of stimulation at early stage of injury. It could promote nerve regeneration and prevent muscle atrophy. Conclusion NMES plays an important role in cl inical appl ication of treating peripheral nerve injury, and implantable stimulation will be the future.
An automatic control system was designed to suppress pathological tremor on wrist joint with two degrees of freedom (DoF) using functional electrical stimulation (FES). The tremor occurring in the wrist flexion-extension and adduction-abduction was expected to be suppressed. A musculoskeletal model of wrist joint was developed to serve as the control plant, which covered four main muscles (extensor carpi radialis longus, extensor carpi ulnaris, flexor carpi radialis, and flexor carpi ulnaris). A second-order mechanical impedance model was used to describe the wrist skeletal dynamics. The core work was to design the controller and a hybrid control strategy was proposed, which combined inverse model based on feed forward control and linear quadratic regulator (LQR) optimal control. Performance of the system was tested under different input conditions (step signal, sinusoidal signal, and real data of a patient). The results indicated that the proposed hybrid controller could attenuate over 94% of the tremor amplitude on multi-DoF wrist joint.
Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.
Transcranial magneto-acoustic electrical stimulation (TMAES) is a novel method of brain nerve regulation and research, which uses induction current generated by the coupling of ultrasound and magnetic field to regulate neural electrical activity in different brain regions. As the second special envoy of nerve signal, calcium plays a key role in nerve signal transmission. In order to investigate the effect of TMAES on prefrontal cortex electrical activity, 15 mice were divided into control group, ultrasound stimulation (TUS) group and TMAES group. The TMAES group received 2.6 W/cm2 and 0.3 T of magnetic induction intensity, the TUS group received only ultrasound stimulation, and the control group received no ultrasound and magnetic field for one week. The calcium ion concentration in the prefrontal cortex of mice was recorded in real time by optical fiber photometric detection technology. The new object recognition experiment was conducted to compare the behavioral differences and the time-frequency distribution of calcium signal in each group. The results showed that the mean value of calcium transient signal in the TMAES group was (4.84 ± 0.11)% within 10 s after the stimulation, which was higher than that in the TUS group (4.40 ± 0.10)% and the control group (4.22 ± 0.08)%, and the waveform of calcium transient signal was slower, suggesting that calcium metabolism was faster. The main energy band of the TMAES group was 0−20 Hz, that of the TUS group was 0−12 Hz and that of the control group was 0−8 Hz. The cognitive index was 0.71 in the TMAES group, 0.63 in the TUS group, and 0.58 in the control group, indicating that both ultrasonic and magneto-acoustic stimulation could improve the cognitive ability of mice, but the effect of the TMAES group was better than that of the TUS group. These results suggest that TMAES can change the calcium homeostasis of prefrontal cortex nerve clusters, regulate the discharge activity of prefrontal nerve clusters, and promote cognitive function. The results of this study provide data support and reference for further exploration of the deep neural mechanism of TMAES.
Neuromuscular electrical stimulation (NMES) has been proven to promote human balance, but research on its impact on motor ability mainly focuses on external physical analysis, with little analysis on the intrinsic neural regulatory mechanisms. This study, for the first time, investigated the effects of NMES on cortical activity and cortico-muscular functional coupling (CMFC) during standing balance. Twelve healthy subjects were recruited in bilateral NMES training, with each session consisting of 60 electrically induced isometric contractions. Electroencephalogram (EEG) signals, electromyogram (EMG) signals, and center of pressure (COP) signals of the foot sole were collected before stimulation, two weeks after stimulation, and four weeks after stimulation while the subjects maintained standing balance. The results showed that NMES training improved subjects' postural stability during standing balance. Additionally, based on the EMG power spectral density (PSD), the κ frequency band was defined, and EEG-EMG time-frequency maximal information coefficients (TFMIC) were calculated. It was found that NMES enhanced functional connectivity between the cortex and lower limb muscles, with varying degrees of increase in β-κ and γ-κ frequency band CMFC after stimulation. Furthermore, sample entropy (SE) of EEG signals also increased after training. The results of this study confirm that NMES training can enhance CMFC and brain activation during standing balance. This study, from the perspective of physiological electrical signals, validates the effectiveness of NMES for balance training and provides objective assessment metrics for the training effects of NMES.
A realizaton project of electrical stimulator aimed at motor dysfunction of stroke is proposed in this paper. Based on neurophysiological biofeedback, this system, using an ARM9 S3C2440 as the core processor, integrates collection and display of surface electromyography (sEMG) signal, as well as neuromuscular electrical stimulation (NMES) into one system. By embedding Linux system, the project is able to use Qt/Embedded as a graphical interface design tool to accomplish the design of stroke rehabilitation apparatus. Experiments showed that this system worked well.
In order to investigate the effect of deep brain stimulation on diseases such as epilepsy, we developed a closed-loop electrical stimulation system using LabVIEW virtual instrument environment and NI data acquisition card. The system was used to detect electrical signals of epileptic seizures automatically and to generate electrical stimuli. We designed a novel automatic detection algorithm of epileptic seizures by combining three features of field potentials: the amplitude, slope and coastline index. Experimental results of rat epileptic model in the hippocampal region showed that the system was able to detect epileptic seizures with an accuracy rate 91.3% and false rate 8.0%. Furthermore, the on-line high frequency electrical stimuli showed a suppression effect on seizures. In addition, the system was adaptive and flexible with multiple work modes, such as automatic and manual modes. Moreover, the simple time-domain algorithm of seizure detection guaranteed the real-time feature of the system and provided an easy-to-use equipment for the experiment researches of epilepsy control by electrical stimulation.
The rectus femoris muscles of rabbits were used as muscle model. The electrical stimulation which resembled the normal motor-unit activity was used to observe its effects on free transferred muscle. After three months, the moist muscle weight (MW), its maximum cross-section area, its contractility and its histochemical characteristics were examined. The results showed that the function and morphology of the muscles were well preserved. These findings might encourage its clinical application.