Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings.

Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints. The dimensionality of this feature vector was then reduced using a linear feature projection composed of projection pursuit and negentropy maximization (PP/NEM). Finally, a time-delayed Kalman filter was used to estimate the ankle and knee joint angles. The PP/NEM approach had a better decoding performance than did other feature projection methods, and all processes were completed within the real-time constraints. These results suggested that the proposed method could be a useful decoding method to provide real-time feedback signals in closed-loop FES systems.

An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode.

Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

Multiunit Activity-Based Real-Time Limb-State Estimation from Dorsal Root Ganglion Recordings.

Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems.

Ankle-Angle Estimation from Blind Source Separated Afferent Activity in the Sciatic Nerve for Closed-Loop Functional Neuromuscular Stimulation System.

Cuff electrode recording has been proposed as a solution to obtain robust feedback signals for closed-loop controlled functional neuromuscular stimulation (FNS) systems. However, single-channel cuff electrode recording requires several electrodes to obtain the feedback signal related to each muscle. In this study, we propose an ankle-angle estimation method in which recording is conducted from the proximal nerve trunk with a multichannel cuff electrode to minimize cuff electrode usage. In experiments, muscle afferent signals were recorded from a rabbit's proximal sciatic nerve trunk using a multichannel cuff electrode, and blind source separation and ankle-angle estimation were performed using fast independent component analysis (PP/FastICA) combined with dynamically driven recurrent neural network (DDRNN). The experimental results indicate that the proposed method has high ankle-angle estimation accuracy for both situations when the ankle motion is generated by position servo system or neuromuscular stimulation. Furthermore, the results confirm that the proposed method is applicable to closed-loop FNS systems to control limb motion.

An Unsorted Spike-Based Pattern Recognition Method for Real-Time Continuous Sensory Event Detection from Dorsal Root Ganglion Recording.

In functional neuromuscular stimulation systems, sensory information-based closed-loop control can be useful for restoring lost function in patients with hemiplegia or quadriplegia. The goal of this study was to detect sensory events from tactile afferent signals continuously in real time using a novel unsorted spike-based pattern recognition method. The tactile afferent signals were recorded with a 16-channel microelectrode in the dorsal root ganglion, and unsorted spike-based feature vectors were extracted as a novel combination of the time and time-frequency domain features. Principal component analysis was used to reduce the dimensionality of the feature vectors, and a multilayer perceptron classifier was used to detect sensory events. The proposed method showed good performance for classification accuracy, and the processing time delay of sensory event detection was less than 200 ms. These results indicated that the proposed method could be applicable for sensory feedback in closed-loop control systems.

An implantable wireless system for muscle afferent recording from the sciatic nerve during functional electrical stimulation.

An implantable wireless system was developed for recording muscle afferent activity and stimulating peripheral nerves with cuff electrodes. The proposed system was fabricated into the nerve cuff electrode, neural amplifier, neural stimulator, and wireless communication system with battery power. The nerve cuff electrode and neural amplifier were designed to improve the signal-to-interference ratio and signal-to-noise ratio. The wireless communication system was designed based on the medical implant communication service regulations to be suitable for implantation. The main function of this system was to extract muscle afferent activity from peripheral nerve during functional electrical stimulation. The cuff electrodes were chronically implanted on the sciatic nerve for recording and on the tibial and peroneal nerves for stimulation. When the extension and flexion movements of ankle joint were elicited from alternative electrical stimuli, the corresponding neural signals and ankle angles were recorded simultaneously. The muscle afferent activity was then extracted from the recorded neural signal through a simple blanking process. The experimental results showed that the ankle movements could be detected from the extracted muscle afferent activity.

Feedback control of electrode offset voltage during functional electrical stimulation.

Control of the electrode offset voltage is an important issue related to the processes of functional electrical stimulation because excess charge accumulation over time damages both the tissue and the electrodes. This paper proposes a new feedback control scheme to regulate the electrode offset voltage to a predetermined reference value. The electrode offset voltage was continuously monitored using a sample-and-hold (S/H) circuit during stimulation and non-stimulation periods. The stimulation current was subsequently adjusted using a proportional-integral (PI) controller to minimise the error between the reference value and the electrode offset voltage. During the stimulation period, the electrode offset voltage was maintained through the S/H circuit, and the PI controller did not affect the amplitude of the stimulation current. In contrast, during the non-stimulation period, the electrode offset voltage was sampled through the S/H circuit and rapidly regulated through the PI controller. The experimental results obtained using a nerve cuff electrode showed that the electrode offset voltage was successfully controlled in terms of the performance specifications, such as the steady- and transient-state responses and the constraint of the controller output. Therefore, the proposed control scheme can potentially be used in various nerve stimulation devices and applications requiring control of the electrode offset voltage.

Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction.

Cutaneous afferent activities recorded by a nerve cuff electrode have been used to detect the stance phase in a functional electrical stimulation system for foot drop correction. However, the implantation procedure was difficult, as the cuff electrode had to be located on the distal branches of a multi-fascicular nerve to exclude muscle afferent and efferent activities. This paper proposes a new gait phase detection scheme that can be applied to a proximal nerve root that includes cutaneous afferent fibers as well as muscle afferent and efferent fibers. To test the feasibility of this scheme, electroneurogram (ENG) signals were measured from the rat sciatic nerve during treadmill walking at several speeds, and the signal properties of the sciatic nerve were analyzed for a comparison with kinematic data from the ankle joint. On the basis of these experiments, a wavelet packet transform was tested to define a feature vector from the sciatic ENG signals according to the gait phases. We also propose a Gaussian mixture model (GMM) classifier and investigate whether it could be used successfully to discriminate feature vectors into the stance and swing phases. In spite of no significant differences in the rectified bin-integrated values between the stance and swing phases, the sciatic ENG signals could be reliably classified using the proposed wavelet packet transform and GMM classification methods.

Tumor-homing poly-siRNA/glycol chitosan self-cross-linked nanoparticles for systemic siRNA delivery in cancer treatment.

The condensed version: Thiolated glycol chitosan can form stable nanoparticles with polymerized siRNAs through charge-charge interactions and self-cross-linking (see scheme). This poly-siRNA/glycol chitosan nanoparticles (psi-TGC) provided sufficient in vivo stability for systemic delivery of siRNAs. Knockdown of tumor proteins by psi-TGC resulted in a reduction in tumor size and vascularization.

Improvement of signal-to-interference ratio and signal-to-noise ratio in nerve cuff electrode systems.

Cuff electrodes are effective for chronic electroneurogram (ENG) recording while minimizing nerve damage. However, the ENG signals are usually contaminated by electromyogram (EMG) activity from the surrounding muscles, stimulus artifacts produced by the electrical stimulation and noise generated in the first stage of the neural signal amplifier. This paper proposed a new cuff electrode to reduce the interference from EMG signals and stimulus artifacts. As a result, when an additional middle electrode was placed at the center of the cuff electrode, a significant improvement in the signal-to-interference ratio was achieved at 11% for the EMG signals and 12% for the stimulus artifacts when compared to a conventional tripolar cuff. Furthermore, a new low-noise amplifier was proposed to improve the signal-to-noise ratio. The circuit was designed based on a noise analysis to minimize the noise, and the results show that the total noise of the amplifier was below 1 µV for a cuff impedance of 1 kΩ and a frequency bandwidth of 300 to 5000 Hz.

Measurement of MMP Activity in Synovial Fluid in Cases of Osteoarthritis and Acute Inflammatory Conditions of the Knee Joints Using a Fluorogenic Peptide Probe-Immobilized Diagnostic Kit.

PURPOSE: A fluorogenic peptide probe-immobilized diagnostic kit was used to analyze MMP activity in the synovial fluids (SFs) from patients with osteoarthritis (OA) and acute inflammatory conditions of the knee joint. METHODS: The MMP diagnostic kit containing a polymer-conjugated MMP probe immobilized on a 96-well plate was utilized for high-throughput screening of MMP activity in SFs from OA patients (n = 33) and patients with acute inflammatory conditions of the knee joint (n = 5). RESULTS: Compared to SF from OA patients, SF from patients with acute inflammatory conditions of the knee joint presented stronger NIR fluorescent signals. In gelatin zymography, most samples from patients with acute inflammatory conditions of the knee joint also displayed 92 kDa (pro-form) MMP-9 and faint 84 kDa (active form) MMP-9, while SF from OA patients did not display detectable MMP-9 activity . CONCLUSION: The presence of a strong fluorescence signal from the MMP diagnostic kit corresponded well with patients with acute inflammatory conditions of the knee joint. The results suggest that our MMP diagnostic kit can be useful in differentiation between early stages of OA and acute inflammatory conditions of the knee joint.

Spontaneous synchronized burst firing of subthalamic nucleus neurons in rat brain slices measured on multi-electrode arrays.

The current study presents an organotypic rat midbrain slice culture that served as a consistent and informative framework, where the STN neurons and their interconnectivity were closely examined with respect to electrophysiological and pharmacological properties. From multi-electrode array recordings, it was found that the majority of STN neurons spontaneously fired in bursts rather than tonically under control conditions, and the neural activity between pairs of burst-firing STN neurons was tightly correlated. This spontaneous synchronized burst firing was also affected by a glutamate receptor antagonist, yet unaffected by a GABA receptor antagonist. Moreover, even when the STN was isolated from all its known external inputs, spontaneous synchronized burst firing was still observed under control conditions and consistently switched to tonic firing following the application of a glutamate receptor antagonist. Therefore, the results indicated the existence of glutamatergic projections to the STN in the slice preparation, and these excitatory synaptic connections appeared to originate from axon collaterals within the STN rather than other basal ganglia nuclei. It could be concluded that the STN neurons and their interconnectivity are essential requirements in the rat brain slice preparation to produce spontaneous synchronized burst firing.

Early diagnosis of arthritis in mice with collagen-induced arthritis, using a fluorogenic matrix metalloproteinase 3-specific polymeric probe.

OBJECTIVE: Early treatment based on an early diagnosis of rheumatoid arthritis (RA) could halt progression of the disease, but early diagnosis is often difficult. Matrix metalloproteinase 3 (MMP-3) is thought to be particularly important in the pathogenesis of RA. The aim of this study was to investigate whether an MMP-3-specific polymeric probe could be used for early diagnosis and for visualizing the progression of arthritis, using a near-infrared fluorescence (NIRF) imaging system. METHODS: The MMP-3-specific polymeric probe was developed by conjugating NIRF dye, MMP substrate peptide, and dark quencher to self-assembled chitosan nanoparticles. One hour after intravenous administration of the probe, fluorescent images of mice with collagen-induced arthritis at different stages of disease development were obtained. The correlation between the fluorescence recovered in in vivo imaging when using an MMP-3-specific polymeric probe and up-regulated MMP-3 activity in the joint tissues was evaluated by Western blotting and immunohistochemical staining. Histologic analysis and micro-computed tomography (micro-CT) were also used to assess arthritis progression. RESULTS: A significantly higher NIRF signal was recovered from arthritic joints compared with normal joints at 14 days after the first immunization, before any erythema or swelling could be observed with the naked eye or any erosion was detected by histologic analysis or micro-CT. The results of immunohistochemical analysis and Western blotting confirmed that the fluorescence recovered in the in vivo imaging was related to up-regulated MMP-3 activity in the joint tissues. CONCLUSION: An MMP-3-specific polymeric probe provided clear early diagnosis of arthritis and visualization of arthritis progression using an NIRF imaging system. This approach could be used for early diagnosis and for monitoring drug and surgical therapies in individual cases.

Conjugate-prior-penalized learning of Gaussian mixture models for multifunction myoelectric hand control.

This paper presents a new learning method for Gaussian mixture models (GMMs) to improve their generalization ability. A traditional maximum a posterior (MAP) parameter estimate is used to achieve regularization based on conjugate priors. Plus, a model order selection criterion is derived from Bayesian-Laplace approaches, using the conjugate priors to measure the uncertainty of the estimated parameters. As a result, the proposed learning method avoids the possibility of convergence toward the boundary of the parameter space, and is also capable of selecting the optimal order for a GMM with more enhanced stability than conventional methods using a flat prior. When applying the proposed learning method to construct a GMM classifier for electromyogram (EMG) pattern recognition, the proposed GMM classifier achieves a high generalization ability and outperforms conventional classifiers in terms of recognition accuracy.

A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.

This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.

A supervised feature projection for real-time multifunction myoelectric hand control.

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMG pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMG signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron (MLP) classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time control system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the myoelectric hand control, are completed within 97 msec.