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BACKGROUND Transcranial magnetic stimulation (TMS), when applied over the primary motor cortex, elicits a motor-evoked potential (MEP) in electromyograms measured from peripheral muscles. MEP amplitude has often been observed to fluctuate trial to trial, even with a constant stimulus. Many factors cause MEP fluctuations in TMS. One of the primary factors is the weak stationarity and instability of cortical activity in the brain, from which we assumed MEP fluctuations originate. We hypothesized that MEP fluctuations are suppressed when TMS is delivered to the primary motor cortex at a time when several electroencephalogram (EEG) channels measured on the scalp are highly similar in the frequency domain. OBJECTIVE We developed a TMS triggering system to suppress MEP fluctuations using EEG coherence analysis, which was performed to detect the EEG signal similarity between the 2 channels in the frequency domain. METHODS Seven healthy adults participated in the experiment to confirm whether the TMS trigger system works adequately, and the mean amplitude and coefficient of the MEP variation were recorded and compared with the values obtained during the control task. We also determined the experimental time under each condition and verified whether it was within the predicted time. RESULTS The coefficient of variation of MEP amplitude decreased in 5 of the 7 participants, and significant differences (P=.02) were confirmed in 2 of the participants according to an F test. The coefficient of variation of the experimental time required for each stimulus after threshold modification was less than that without threshold modification, and a significant difference (PF test. CONCLUSIONS We found that MEP could be suppressed using the system developed in this study and that the TMS trigger system could also stabilize the experimental time by changing the triggering threshold automatically.

Transcranial magnetic stimulation (TMS) over the cerebellum facilitates the spinal reflex in healthy humans. The aim of this study was to investigate whether such cerebellar spinal facilitation (CSpF) appears in patients with spinocerebellar ataxia (SCA) presenting with atrophy in the cerebellar gray matter and dentate nucleus. One patient with SCA type 6 and another with SCA type 31 participated in this study. TMS over the right primary motor cortex was used to induce motor-evoked potentials in the right first dorsal interosseous muscle, which were detected using electromyography. Conditioning TMS using interstimulus intervals of 1-8 ms was performed over the right cerebellum as a test to measure cerebellar brain inhibition (CBI). To assess the H-reflex and the M-wave recruitment curve of the right soleus muscle, we performed electrical stimulation of the right tibial nerve. The stimulation intensity was set to that at the center of the H-reflex curve of the ascending limb. To measure CSpF, we delivered TMS over the right cerebellum 100, 110, 120, and 130 ms before the right tibial nerve stimulation. Voxel-based morphometry was used to verify the presence of atrophy in the cerebellar gray matter and dentate nucleus. CBI was absent in both cases. However, a significant facilitation of the H-reflex occurred with an interstimulus interval of 120 ms in both cases. These findings indicate that the pathways associated with the induction of CSpF and CBI are different, and that the cerebellar gray matter and dentate nucleus are not needed for the induction of CSpF. The possible origin of CSpF may be examined by stimulation of other cerebellar deep nuclei or the brainstem.

Modeling the evolution of user preferences and item attributes in a dynamic social network is important because it is the basis for many applications, including recommendation systems and user behavior analysis. This study introduces a comprehensive general neural framework with several optimal strategies to jointly model the evolution of user preferences and item attributes in dynamic social networks. Preliminary experimental results conducted on real-world datasets demonstrate that our model performs better than the state-of-the-art methods.

Surface electromyogram (sEMG) signals have been used to control multifunctional prosthetic hands. Researchers usually focused on the use of several channels with sEMG signals to identify more hand motions without limiting the number of sEMG sensors. However, the residual muscles of an amputee are limited. Therefore, the point of a successful recognition system is to decrease the channels of sEMG signals to classify more hand motions. To achieve this goal, we proposed a hand motion recognition system, in which three channels of sEMG signals can classify nine hand motions. In this recognition system, the time domain features, root mean square ratio (RMSR) and autoregressive (AR) model, were selected to extract the features of the sEMG signals as compared with the time-frequency domain features. Furthermore, the linear discriminant analysis (LDA) was adopted as the classifier. Consequently, the average accuracy rate of the presented system was 91.46%. Therefore, the proposed algorithms in this paper can be reasonably feasible for prosthetic hands.

Conventional brain-computer interface (BCI) systems can control a robot to execute a certain task according to human intention. However, in the actual application, it is still difficult for users to control multiple robots to execute the cooperative service tasks, including mobility, manipulation and delivery. At the same time, using BCI to control robots for a long time may bring users inevitable fatigue. To solve these dilemmas, we design an SSVEP-based BCI system for a manipulator and a mobile robot to work cooperatively. To reduce the user's fatigue, a ceiling feature based vision assisted navigation module is developed to facilitate the robot moving process. Considering characteristics of electroencephalograph signals, steady-state visual evoked potentials (SSVEP) are employed as input signals for the BCI system, and seven visual stimuli are modulated as control commands. Consequently, this SSVEP-based BCI system can generate four commands to control the movement of Turtlebot mobile robot, one switch command for the visual assisted navigation module, and two commands for the grasp task of UR5 manipulator. Due to the ceiling feature based vision assisted navigation module, the mobile robot can move effectively without human interference, which may reduce the user's fatigue. To evaluate the performance of this system, three subjects were recruited to participate in the experiments. All the subjects succeeded to execute the cooperative control of multiple robots with the vision assisted navigation module. According to the experimental results, the developed BCI system can effectively realize the cooperative control of multiple robots without much fatigue.

Network representation is the basis of many applications and of extensive interest in various fields such as information retrieval, social network analysis, and recommendation systems. Majority of previous methods on network representation only considered incomplete aspects of the problem, such as link structure, node information, or partial integration. The present paper proposes a comprehensive network representation model, which seamlessly integrates the text information, node label, and first-order and second-order proximity of a network. The effectiveness of the introduced strategies is experimentally evaluated. Results demonstrate that our method is better than state-of-the-art techniques.

The behavior analysis of animal has a key role in biology and robotics. In this paper, we choose the rat as analysis object. For understanding and grasping the behavior of rat, we set up an automatic motion tracking and data analysis system for a rat. This system includes three subsystems: inertial data acquisition, image tracking and real-time data analysis. The inertial subsystem uses one CC2530 development board and one small modular development board which integrates MPU6050 and electrical stimulator, and the inertial data collected by MPU6050 and the controls parameter sent by experimenter communicate by Zig-Bee wireless transmission module to realize the two-way transmission. The image tracking subsystem recognizes and tracks the rat by the camera. Besides, the inertial data and image tracking are time-synchronized. Finally, we program a data analysis interface by Matlab to conduct real-time data analysis. According to the tests, the automatic motion tracking and data analysis system can recognize the rat with a successful rate of 97%, combined with the inertial data. At the same time, we can conduct the real-time data analysis to simulate the attitude of the rat in the interface.

This paper intends to design a system which acquires the trainer's motion and force information in order to manipulate a robot arm applied for rehabilitations. Patients who suffering physical disability also can receive the professorial guiding and cheirapsis even excellent trainers are very busy and insufficient. The key point of this article is data acquisition and reconstruction of the movement of the upper limb by controlling the UR5 robot arm. Upper limb's postures are sensed by Inertial Measurement Unit (IMU) and transferred to STM32 microcontroller using I2C communication protocol. We employed the STM32 microcontroller to calculate attitude angles of both the upper arm and the forearm. And the method with using quaternions to calculate attitude angles is detailedly expounded in this paper. Besides, we employed the MYO armband to acquire upper limb's surface electromyography (sEMG) signals for estimating the muscle force of the upper limb. To verify the feasibility of the proposed system, we make three experiments including analyzing fluctuation range of the attitude angles from IMU signals, classifying muscle force using sEMG signals, and evaluating the effect of motion reconstruction. And the results show that the fluctuation range of acquired data are less than 1 degree, 4 typical motions of upper limb can be reconstructed. The proposed system can be used to reconstruct some upper limb's movement.

We reported that cerebellar transcranial magnetic stimulation (TMS) facilitates spinal reflex in healthy humans. The aim of this study was to investigate whether the “cerebellar” spinal facilitation (CSpF) appears in patients with spinocerebellar ataxia (SCA) with atrophy in the cerebellar cortex and dentate nucleus (DN). Two patients with SCA (one was SCA6 and the other was SCA31), participated in this study. Cerebellar inhibition (CBI) was tested using paired-TMS-paradigm with interstimulus intervals (ISI) of 1–8 ms in the right first dorsal interosseous muscle. To measure CSpF, we delivered TMS over the right cerebellum 100–130 ms before right tibial nerve stimulation, and CSpF was calculated with obtained conditioned/unconditioned H-reflex amplitude in the right soleus muscle. Voxel-based morphometry was used to verify the atrophy in cerebellar cortex and the DN. The results showed absence of CBI, but there was a significant facilitation of the H-reflex which occurred in the 120 ms ISI in both patients. These findings indicate that the pathways associated with the induction of CSpF and CBI are different, and that the cerebellar cortex and the DN are not required for inducing CSpF. Possible generator of CSpF could be other deep cerebellar nuclei or the brain stem.

Motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) vary in their amplitude from trial to trial. To investigate the functions of motor cortex by TMS, it is necessary to confirm the causal relationship between stimulated sites and variable MEPs. We created artificial neural networks to classify sets of variable MEP signals and finger forces into the corresponding stimulated sites. We conducted TMS at three different positions over M1 and measured MEPs of hand and forearm muscles and forces of the index finger in four subjects. We estimated the sites within motor cortex stimulated by TMS based on cortical columnar structure and nerve excitation properties. Finally, we tried to classify the various MEPs and finger forces into three groups using artificial neural networks. MEPs and finger forces varied from trial to trial, even if the stimulating coil was fixed on the subject's head. Our proposed neural network was able to identify the MEPs and finger forces with the corresponding stimulated sites in M1. We proposed the artificial neural networks to confirm the TMS-stimulated sites using various MEPs and evoked finger forces.

In the present study, muscle activity was measured after transcranial magnetic stimulation (TMS) was applied over the primary motor cortex during finger tapping. The duration of finger tapping was increased by the application of TMS. A remarkable change in electromyogram (EMG) of hand muscles was also observed during and after finger tapping. The silent period, in which voluntary EMG during the finger tapping was suppressed by TMS, was observed. Integral EMG revealed that TMS increased muscle activity at the completion of the finger tap after silent period. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

An estimation method for multiple sources of magnetoencephalogram is proposed. In the proposed method, the assumed dipoles are arranged on and oriented perpendicular to the surface of the cerebral cortex based on the hypothesis of the functional columnar structure. Since the positions and orientations of the assumed dipoles are given beforehand, the magnetic fields that originate from the assumed dipoles are predictable. Using a coefficient of determination between the predicted and measured magnetic fields as a criterion, the electric sources of the N20m component of the somatosensory evoke fields when the central nerves of the wrists are stimulated bilaterally and the N100m component of the auditory evoked fields are localized. The positions of the dipoles are close to those localized by the moving dipole method.

Somatosensory cortex receives afferent inputs from skeletal muscles and joints while a voluntary movement is conducted. Although this sensory feedback may regulate the efferent motor control signals generated in the motor cortex, the relationship between the afferent sensory signals and the efferent motor signals is still unclear. In this study, we investigated the relationship between the afferent signals elicited by an electrical stimulus of a peripheral nerve and the efferent signals produced by a transcranial magnetic stimulation (TMS) of the motor cortex. The changes of motor evoked potentials (MEPs) elicited by TMS following an electrical stimulus of a median nerve were observed. The results showed that the MEPs were significantly attenuated when the inter-stimulus interval (ISI) between the electrical stimulus and the TMS was 20 ms, and that the MEPs were significantly enhanced when the ISI was longer than 35 ms. Furthermore, the brain condition which affected the MEPs was evaluated with the somatosensory evoked fields (SEFs) measured with magneto-encephalography (MEG). It was suggested that the activation and direction of the current dipole in the primary somatosensory cortex was related to the effect of the afferent signals on the motor function.

Strength–Duration Curves (S–D Curves) for the intra-urethral sphincter muscle of the rat were measured using the rectangular waveform and the pseudo-eddy current waveform, which approximate the stimulation current waveform on magnetic stimulation. Finally, the pulse duration for minimum energy using magnetic stimulation is obtained from the measurement results of the S–D curve. The eddy current waveform differs from the rectangular current waveform commonly used in electrical applications. No basic study has examined the effects of magnetic stimulation on threshold values and other aspects of the eddy current waveforms. Magnetic stimulation has been reported to have good efficacy in the treatment of urinary incontinence. It is desirable that the eddy current density produced by a change in the strength of a magnetic field be greater than the excitability threshold of the muscles or nerves to be stimulated. However, effective magnetic stimulation requires the input of a large current into the stimulation coil. The requisite equipment is therefore large and costly, and so it is necessary to develop magnetic stimulators that can more efficiently stimulate tissues and that are more convenient to use than those that are currently available.

It is reported that magnetic stimulation is efficacious in treating urinary incontinence. Magnetic stimulation has two main advantages over electrical stimulation: it can be applied without removal of clothes, and it involves little pain. However, the distribution of current in the living body is not clearly known. We therefore attempted to create a computer simulation model of the female abdomen and to simulate the distribution of induced current on the basis of tissue conductivity. This report compares electrical with magnetic stimulation.

This study investigated current distribution in the female abdomen using experimental and computer models of magnetic stimulation for treatment of urinary incontinence. Four experimental models and two computer models were used to simulate current distributions. In the performed computations of eddy current distribution, 18 cross-sectional images (interslice gap, 10 mm; slice numbers 750-920) of a female pelvic region from the NPAC/OLDA Visible Human Viewer were used. The influence of tissue inhomogeneity on current distribution were investigated using experimental models. In the region of pelvic floor muscles at depths of 20 mm and 50 mm from the experimental model, analysis of eddy current distribution could not be performed as a homogeneous conductor due to large differences in angles and summation of currents between Models I and IV. Within the region of the bladder and other organs at a depth of 100 mm from the experimental model, the pelvis and other tissues exerted little influence on current distribution.

Transcranial magnetic stimulation (TMS) is a non-invasive method for stimulating the cortical neurons in the brain. The eddy current distribution is induced by the time-varying magnetic field of the magnetic coil used in the TMS. The brain neurons can be excited by the eddy current stimulation. The figure-of-eight coil in the TMS is used for the focal stimulation. The brain site beneath the central point of the figure-of-eight coil is conventionally determined as the stimulating site. However, it is difficult to find the optimal position to be stimulated by changing the coil. In this study, we propose an optimization method of the current distribution using a magnetic substance, without varying the coil's position. We verify our method by a computer simulation.

In this study, a method for current dipole estimation with spatial interpolation of measured magnetic fields in magnetoencephalogram (MEG) is proposed. This method applies virtual magnetic (M v ) sensors uniformly interpolated in the surface comprised of MEG (M r ) sensors. Orientations and output signals of the virtual magnetic sensors are calculated by a spatial linear interpolation based on those of real magnetic sensors. For determining an optimal value of regularization parameter (α) in minimum norm inverse solutions, M v sensors are divided into some groups randomly. Quantities of magnetic field (b k ) at the M r sensors are calculated by estimated dipoles with each group of M v sensors. α is determined by minimization of the total sum of differences between b k and the output signals measured by M r sensors. In order to confirm the validity of the proposed method, we estimated source distribution of N20m components of unilateral and bilateral somatosensory evoked fields (SEFs) measured by a 160-channel axial SQUID gradiometer system, when subject’s right and/or left median nerve was electrically stimulated. The proposed method was able to estimate source distribution in the primary somatosensory cortex (S1). Furthermore, the source distribution estimated by the proposed method tended to be more confined than that estimated by a cross validation error method. These results indicate that the virtually improved spatial resolution of MEG data can estimate more confined source distribution.

In this study, the effect of the transcranial magnetic stimulation (TMS) on the continuous voluntary movement of the finger was investigated. We measured the trajectory of the thumb tip and electromyogram (EMG) of the hand and forearm muscles after the TMS of the primary motor cortex area (M1) during continuous voluntary rotation of the thumb. The results showed that the velocity of the thumb tip was accelerated right after the TMS followed by the deceleration. It was indicated that enhance and suppression of the muscle activity by the TMS resulted in the change of the finger velocity in the voluntary movement.

We developed a precise coil positioning system for determining stimulated sites in transcranial magnetic stimulation (TMS) by using a multi-articular mechanical arm. A figure-of-eight coil used in TMS can induce a focal electric field beneath the coils center in the cortex; therefore, the point on the cortex beneath the center of the figure-of-eight coil has been conventionally regarded as a stimulating site. However, a stimulating site determined in this manner is not believed to be plausible, because the electric field induced in TMS is dispersed over the brain vectorially. Our system was developed on the basis of the cortical structure of the brain cortex and the direction of the induced electric field. In order to verify the system accuracy, Motor Evoked Potentials (MEPs) elicited by TMS at the hand muscles innervated by the primary motor cortex (M1) were measured as electromyogram (EMG). And functional vectorial map of the excitability of M1 in the resting state was obtained by rotating the stimulation coil at a constant location.

We proposed an artificial neural network to confirm the location of stimulating coil in transcranial magnetic stimulation (TMS) from data of finger force and electromyography (EMG). In experiments, finger forces of the right index finger and motor evoked potentials (MEPs) of the muscles involved in the generation of the finger forces were measured when the primary motor cortex in the left hemisphere of the cerebrum was stimulated by TMS. The measured finger forces and MEPs varied trial by trial although the stimulating coil and subject's head were fixed. The neural network learned the mapping from the MEPs and/or the finger forces to the coil location. After sufficient learning, the neural network was able to classify unlearned MEPs and finger forces into corresponding coil locations.

Computer simulation and myocardial cell models were used to evaluate a low-energy defibrillation technique. A generated spiral wave, considered to be a mechanism of fibrillation, and fibrillation were investigated using two myocardial sheet models: a two-dimensional computer simulation model and a two-dimensional experimental model. A new defibrillation technique that has few side effects, which are induced by the current passing into the patient's body, on cardiac muscle is desired. The purpose of the present study is to conduct a basic investigation into an efficient defibrillation method. In order to evaluate the defibrillation method, the propagation of excitation in the myocardial sheet is measured during the normal state and during fibrillation, respectively. The advantages of the low-energy defibrillation technique are then discussed based on the stimulation timing.

Electrical stimulation has been reported to have good efficacy in treating urinary incontinence. Recently, magnetic stimulation was also reported to be effective for the treatment of urinary incontinence. The current distributions in the living body induced by these stimulation methods have not yet been shown. Therefore, we attempted to develop computer and experimental models of the female abdomen and to simulate the distribution of induced current based on the tissue conductivity. The present paper describes the developed experimental and computer models of the female abdomen and presents an analysis of the distributions of the respective induced currents. Finally, the authors compared the current distributions in the body produced by the electrical and magnetic stimulation methods in order to determine which method of stimulation is more efficient for the treatment of urinary incontinence.

Wavelet analysis of the finger-tip photo-plethysmogram (PTG) was performed in order to quantify the stress stage. From the result of the wavelet transform of the PTG, we proposed stress index, with which quantification of the mental stress level was easy. In order to confirm the validity of this index, we compared the stress indexes between the stress and resting stages. The relationship between the stress index and the heart rate variability (HRV) was also investigated. The obtained results indicated that using the wavelet transform in analysis showed a remarkable change in the stress index from the resting stage to the stress stage. This change appears in the tidal wave area of the PTG.

The present study proposes a new stress evaluation technique using the photoplethysmogram (PTG). Heart rate variability (HRV) is often used to evaluate mental stress. HRV can be measured from an electrocardiogram (ECG). The frequency variability of HRV and mental stress response are related. PTG also indicates changes in emotional response. PTG can easily be measured without body surface electrodes. This method is less invasive than ECG measurement. We attempt herein to evaluate mental stress by wavelet analysis of the PTG. PTG was measured in the resting and mental stress states, and wavelet transformed PTGs were compared. In nine out of ten subjects, the wavelet result for PTG revealed a decrease in the frequency band.

There is increasing interest in the use of magnetic stimulation as a modality for clinical examination and therapy. It is desirable that the eddy current density produced by a change in the strength of a magnetic field should be greater than the excitability threshold of the muscles or nerves to be stimulated. The eddy current waveform differs from rectangular current waveform common in electrical applications. Little basic study has been done on the effects of magnetic stimulation on threshold values, chronaxies and other aspects of the eddy current waveforms. In this paper, the authors have attempted to measure the strength-duration curve (S-D curve) of eddy currents on the sural muscle of the bullfrog and have proposed a new S-D curve formula for eddy currents in place of Weiss's equation for rectangular current waveforms.