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Cardiac patches are widely investigated to repair or regenerate diseased and aging cardiac tissues. While numerous studies looked into engineering the biochemical/biomechanical/cellular microenvironment and components in the heart tissue, the changes induced by cardiac patches and how they should be controlled to promote cardiac tissue repair/regeneration remains an important yet untapped direction, especially immunological responses. In this study, we designed and fabricated a bilaminated cardiac patch based on extracellular matrix (ECM) materials loaded with the extracellular vesicles (EVs) derived from mesenchymal stromal cells. The function of the biological material to modulate the injury-related microenvironment in a cardiac infarction model in mice was investigated. The study showed that the treatment of EV-ECM patches to the infarcted area increased the level of immunomodulatory major histocompatibility complex class II

Mimicking and leveraging biological structures and materials provide important approaches to develop functional vehicles for drug delivery. Taking advantage of the affinity and adhesion between the activated endothelial cells and innate immune cells during inflammatory responses, hybrid polyester nanoparticles coated with endothelial cell membranes (EM-P) containing adhesion molecules were fabricated and their capability as vehicles to travel to the acute injury sites through leukocyte-mediated processes was investigated. The in vivo studies and quantitative analyses performed through the lung-inflammation mouse models demonstrated that the EM-Ps preferentially interacted with the neutrophils and monocytes in the circulation and the cellular membrane-based biosurface improved the nanoparticle transportation to the inflamed lung possibly via the motility of neutrophils. Utilizing the transgenic zebrafish model, the leukocyte-mediated transportation and biodistribution of EM-Ps were further visualized in real time at the whole-organism level. Endothelial membranes provided a new biosurface for developing biomimetic vehicles to allow the immune cell-mediated transportation and may enable advanced systems for active and highly efficient drug delivery.

SummaryGoal-directed behaviors involve distributed brain networks. The small size of the mouse brain makes it amenable to manipulations of neural activity dispersed across brain areas, but existing optogenetic methods serially test a few brain regions at a time, which slows comprehensive mapping of distributed networks. Laborious operant conditioning training required for most experimental paradigms exacerbates this bottleneck. We present an autonomous workflow to survey the involvement of brain regions at scale during operant behaviors in mice. Naïve mice living in a home-cage system learned voluntary head-fixation (>1 hour/day) and performed difficult decision-making tasks, including contingency reversals, for 2 months without human supervision. We incorporated an optogenetic approach to manipulate activity in deep brain regions through intact skull during home-cage behavior. To demonstrate the utility of this approach, we tested dozens of mice in parallel unsupervised optogenetic experiments, revealing multiple regions in cortex, striatum, and superior colliculus involved in tactile decision-making.

New exosome-laden scaffolds shed light on the immune cell activities that benefit tissue repair., Designing scaffolds capable of inducing and guiding appropriate immune responses holds promise for tissue repair/regeneration. Biofunctional scaffolds were here prepared by immobilizing mesenchymal stromal exosomes onto fibrous polyester materials and allowed cell-mediated delivery of membrane-bound vesicles. Quantitative cell-level analyses revealed that immune cells dominated the uptake of exosomes from scaffolds in vivo, with materials and exosomes acting as the recruiter and trainer for immune cells, respectively, to synergistically promote beneficial macrophage and regulatory T cell responses in skin wounds in mice. Adaptive T helper cell responses were found active in remote immune organs, and exosome-laden scaffolds facilitated tissue repair in large skin injury models. This study demonstrated important mechanisms involved in local and systemic immune responses to biological implants, and understanding tissue-reparative immunomodulation may guide the design of new biofunctional scaffolds.

The relationship between the period of venture capital exit and changes in corporate performance was verified using data for the years before and after IPO for companies that exited venture capital IPO in 2017. Multiple regression analysis was applied, and the results show that a longer period of venture capital exit corresponded to a more significant decline in corporate performance. These conclusions help elucidate the relationship between the IPO exit of venture investment companies and the performance of invested companies. These results contribute to our understanding of venture investment exit and provide practical suggestions for the supervision of venture capital exit.

A novel nitrogen- and sulfur-doped carbon quantum dot-modified MIL-101(Fe) (N,S:CQD/MIL-101(Fe)) heterojunction for visible light-induced activation of persulfate (PS) for bisphenol A (BPA) degradation was researched in this study. The photocatalytic performance of BPA degradation was remarkably promoted by the composite material under the simultaneous action of PS and visible light irradiation. BPA photodegradation (100%) was achieved over 60 min with 0.4 g/L of the N,S:CQD/MIL-101(Fe) composite and 3 mM PS at natural pH. The degradation kinetics rate constants (k) approached 3.6 times higher than that of MIL-101(Fe). The enhanced performance in BPA degradation can be ascribed to the interfacial transference of photoinduced electrons from N,S:CQD to MIL-101(Fe), leading to the valid separation of electron-hole pairs and greatly accelerating PS activation to generate highly reactive SO4•−. Both holes (h+), sulfate radicals (SO4•−), superoxide radicals (•O2−), and hydroxyl radicals (•OH) contributed to BPA degradation according to radical quenching experiments. N,S:CQD/MIL-101(Fe) also exhibited great reusability and excellent catalytic performance after four consecutive cycles of use. This work provides valid organic contaminant degradation under the dual action of visible light and PS with a novel perspective on the fabrication of heterojunction photocatalysts ground on Fe-based MOFs.

Iron oxide @ poly(m-phenylenediamine) (Z) particles (Fe3O4@PmPD(Z)) were synthesized by in-situ polymerization of PmPD on the Fe3O4 nanoparticles via ZIF-8 intermediate platform to remove Cr(VI) from wastewater. Analyses of FT-IR, TEM, etc. were performed to evaluate the morphology and structure of synthesized material, and the results confirmed the successful synthesis of Fe3O4@PmPD(Z). In addition, the TEM and BET analyses results show that the PmPD shell is a flocculating structure, and the increased specific surface area increases the contact with Cr(VI). The Fe3O4@PmPD(Z) exhibits much better Cr(VI) adsorption performance than Fe3O4@PmPD, and its adsorption capacity is increased by about 30%, the maximum Cr(VI) adsorption capacity can reach 371.75 mg g−1 at 318 K. According to FT-IR, XPS, and EDS, the removal mechanisms can be attributed to the redox reaction of Cr(Ⅵ) and NH to form N and Cr(Ⅲ), and N shares electron pair with Cr(Ⅲ) to form chelation. At the same time, the appearance of N + also contributes to the removal of Cr(Ⅵ) by generating electrostatic attraction with the negatively charged HCrO4-. Besides, the Fe3O4@PmPD(Z) also exhibits excellent properties of recovery and reuse. Therefore, the current work proves that Fe3O4@PmPD(Z) has the potential to remove heavy metals in wastewater.

Brain–machine interface technology has attracted increasing interest for implementing advanced applications to restore or augment human sensorimotor and other cognitive functions. The BMI technology also represents a powerful tool for addressing fundamental questions in neuroscience. During the last few decades, significant advances have been made in the development of BMI systems based on invasive recording technologies such as intracortical microelectrode arrays or electrocorticography. Despite its late start, China has been making rapid progress in developing key technologies and systems to support invasive BMI researches on nonprimates, nonhuman primates, and humans, for the purposes of rehabilitation engineering, clinical trials, or basic research. In this chapter, we review recent progresses in invasive BMI made by multidisciplinary researchers of scientists, engineers, and clinicians in China and propose potential development trends and challenges of this technology to translate advances into clinical applications.

Reaching and grasping are highly-coupled movements, and their underlying neural dynamics have been widely studied in the last decade. To distinguish reaching and grasping encodings, it is essential to present different object identities independent of their positions. Presented here is the design of an automatic apparatus that is assembled with a turning table and three-dimensional (3D) translational device to achieve this goal. The turning table switches different objects corresponding to different grip types while the 3D translational device transports the turning table in 3D space. Both are driven independently by motors so that the target position and object are combined arbitrarily. Meanwhile, wrist trajectory and grip types are recorded via the motion capture system and touch sensors, respectively. Furthermore, representative results that demonstrate successfully trained monkey using this system are described. It is expected that this apparatus will facilitate researchers to study kinematics, neural principles, and brain-machine interfaces related to upper limb function.

Background The neural principles underlying reaching and grasping movements have been studied extensively in primates for decades. However, few experimental apparatuses have been developed to enable a flexible combination of reaching and grasping in one task in three-dimensional (3D) space. New method By combining a custom turning table with a 3D translational device, we have developed a highly flexible apparatus that enables the subject to reach multiple positions in 3D space, and grasp differently shaped objects with multiple grip types in each position. Meanwhile, hand trajectory and grip types can be recorded via optical motion tracking cameras and touch sensors, respectively. Results We have used the apparatus to successfully train a macaque monkey to accomplish a visually-guided reach-to-grasp task, in which, six objects, fixed on the turning table, were grasped appropriately when they were transported to multiple positions in 3D space. A preliminary analysis of neural signals recorded in primary motor cortex, shows that plenty of neurons exhibit significant tuning to both target position and grip type. Comparison with existing method(s) Our apparatus realizes an arbitrary combination of parameterized reaching and grasping movements in a single task, which were usually separated or fixed in other systems. Meanwhile, the apparatus has high expansibility in terms of dynamic range, object shapes and applicable subjects. Conclusions The apparatus provides a valuable platform to study upper limb functions in behavioral and neurophysiological studies, and may facilitate simultaneous reconstruction of reaching and grasping movements in brain-machine interfaces (BMIs).

Copolymerization of propargylether having antipyrine group (PT) and N-(tert-butoxycarbonyl)-l-valine-N-propargylamide (LA) was conducted with (nbd)Rh+[η6-C6H5B−(C6H5)3] as a catalyst to obtain novel antipyrine-functionalized chiral copolymer. The controllable secondary structure of the copolymers by different unit ratio or solvent environment led to a controlled fluorescence of the side-chain antipyrine. Poly(LA88-co-PT12) exhibited a large specific rotation and a circular dichroism (CD) signal, while it emitted very stronger fluorescence. From CD and ultraviolet–visible spectra, the regular structure of poly(LA88-co-PT12) was destroyed, and the random coil was formed with temperature increase. The helical conformation of poly(LA75-co-PT25) disappeared by the addition of MeOH to CHCl3 solution, while the fluorescence signal also became weaker than in CHCl3 solution. It is suggested that the copolymer conformation much influenced the performance of chromophores. In the present study, the helix conformation could induce fluorescence enhancement. Copyright © 2015 John Wiley & Sons, Ltd.

BACKGROUND: Ghrelin was associated with several of cancers. The conflict results of SNPs with GHRL and GHSR gene were demonstrated in different studies. Thus, this meta-analysis is to evaluate the associations. METHODS: Systematic literature search was done on PubMed database up to October 2013. We used odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of the association by a fixed-effect model and a random-effect model. RESULTS:A total of 7 studies, which included 3 studies for breast cancer, 2for colorectal cancer, 1 for hepatocellular carcinoma, 1 for esophageal cancer and 1 for Non-Hodgkin lymphoma. When analyzed all the GHRL SNPs with all kinds of cancers, there was significantly difference with cancer patients compared with controls (Recessive model: OR 0.938, 95% CI 0.890–0.989, p = 0.017), while no significant difference was existed in the additive model (OR 0.9903, 95% CI 0.957–1.024, p = 0.558) and dominant model (OR 1.014, 95% CI 0.970–1.061, p = 0.536). When analyzed all the GHSR SNPs with all kinds of cancers, no significant difference was observed. CONCLUSION: Our results suggest that the SNP with GHRL and GHSR might be weaker association with cancer risk, especially with breast cancer risk.

Objective.Previous studies have demonstrated that target direction information presented by the dorsal premotor cortex (PMd) during movement planning could be incorporated into neural decoder for achieving better decoding performance. It is still unknown whether the neural decoder combined with only target direction could work in more complex tasks where obstacles impeded direct reaching paths.Methods.In this study, spike activities were collected from the PMd of two monkeys when performing a delayed obstacle-avoidance task. We examined how target direction and intended movement selection were encoded in neuron population activities of the PMd during movement planning. The decoding performances of movement trajectory were compared for three neural decoders with no prior knowledge, or only target direction, or both target direction and intended movement selection integrated into a mixture of trajectory model (MTM).Results.We found that not only target direction but also intended movement selection was presented in neural activities of the PMd during movement planning. It was further confirmed by quantitative analysis. Combined with prior knowledge, the trajectory decoder achieved the best performance among three decoders.Conclusion.Recruiting prior knowledge about target direction and intended movement selection extracted from the PMd could enhance the decoding performance of hand trajectory in indirect reaching movement.

Brain machine interfaces (BMIs) have demonstrated lots of successful arm-related reach decoding in past decades, which provide a new hope for restoring the lost motor functions for the disabled. On the other hand, the more sophisticated hand grasp movement, which is more fundamental and crucial for daily life, was less referred. Current state of arts has specified some grasp related brain areas and offline decoding results; however, online decoding grasp movement and real-time neuroprosthetic control have not been systematically investigated. In this study, we obtained neural data from the dorsal premotor cortex (PMd) when monkey reaching and grasping one of four differently shaped objects following visual cues. The four grasp gesture types with an additional resting state were classified asynchronously using a fuzzy k-nearest neighbor model, and an artificial hand was controlled online using a shared control strategy. The results showed that most of the neurons in PMd are tuned by reach and grasp movement, using which we get a high average offline decoding accuracy of 97.1%. In the online demonstration, the instantaneous status of monkey grasping could be extracted successfully to control the artificial hand, with an event-wise accuracy of 85.1%. Overall, our results inspect the neural firing along the time course of grasp and for the first time enables asynchronous neural control of a prosthetic hand, which underline a feasible hand neural prosthesis in BMIs.

The loss of hand function, due to amputation or neurological injuries, severely debilitates physically and psychosocially. The most evident and critical impairment after upper limb amputation or neurological injury like brachial plexus or spinal cord injury is the loss of prehension, i.e., the ability to perform those movements in which an object is seized and held partially or wholly within the compass of the hand.

It has been suggested that the brain adopts a simplified strategy to coordinate a large number of degrees of freedom in motor control. Synergies have been proposed as a strategy to produce movements by recruitment of a small number of fixed modular patterns. However, there is no direct support for a synergistic organization of the brain itself. In this study, we recorded neural activities from the dorsal premotor cortex (PMd) of monkeys trained to reach and grasp differently shaped objects (grasping task) or the same object in different positions (reaching task). Non-negative matrix factorization (NNMF) was applied to the neural data to extract neural synergies, whose functional roles were verified in several ways. We found that motor cortex used similar neural synergies for grasping different objects; combining only a few of the synergies accounted for most of the variance in the original data. When used for single-trial task decoding, the synergy coefficients performed as well and robustly as the original data in both tasks. The synergy amplitudes for each unit were significantly correlated with the corresponding neuron's firing rate. In addition, we also observed synergies shared between tasks and task-specific synergies, as shown before for muscle synergies. Altogether, we demonstrated that neural synergies are effective in describing neural population activity during reach to grasp movements and provide a new tool for interpreting neural data for movement control.

This study focuses on the estimation of kinematic and kinetic information during two-digit grasping using frequency decoding of motor cortex spike trains for brain machine interface applications. Neural data were recorded by a 100-microelectrode array implanted in the motor cortex of one monkey performing instructed reach-grasp-and-pull movements. Decoding of neural data was performed by two different algorithms: i) through Artificial Neural Networks (ANN) consisting of a multi layer perceptron (MLP), and ii) by a Support Vector Machine (SVM) with linear kernel function. Decoding aimed at classifying the upcoming grip type (precision grip vs. side grip) as well as the required grip force (low vs. high). We then used the decoded information to reproduce the monkey's movement on a robotic platform consisting of a two-finger, eleven degrees of freedom (DoF) robotic hand carried by a six DoF robotic arm. The results show that 1) in terms of performance there was no significant difference between ANN and SVM prediction. Both algorithms can be used for frequency decoding of multiple motor cortex spike trains: good performance was found for grip type prediction, less so for grip force. 2) For both algorithms the prediction error was significantly dependent on the position of the input time window associated to different stages of the instructed grasp movement. 3) The lower performance of grasp force prediction was improved by optimizing the neuronal population size presented to the ANN input layer on the basis of information redundancy.

Novel gold-shell nanoparticles (pH-GSNPs) are designed for the first time, which exhibit drug leakage-free behavior in a physiological environment, while achieving rapid drug release and remarkable aggregation for the nanogold interlayer of pH-GSNPs to shift their absorption to far-red and NIR as a photothermal agent in the intracellular microenvironment.

Objective. Recent studies have shown that dorsal premotor cortex (PMd), a cortical area in the dorsomedial grasp pathway, is involved in grasp movements. However, the neural ensemble firing property of PMd during grasp movements and the extent to which it can be used for grasp decoding are still unclear. Approach. To address these issues, we used multielectrode arrays to record both spike and local field potential (LFP) signals in PMd in macaque monkeys performing reaching and grasping of one of four differently shaped objects. Main results. Single and population neuronal activity showed distinct patterns during execution of different grip types. Cluster analysis of neural ensemble signals indicated that the grasp related patterns emerged soon (200–300 ms) after the go cue signal, and faded away during the hold period. The timing and duration of the patterns varied depending on the behaviors of individual monkey. Application of support vector machine model to stable activity patterns revealed classification accuracies of 94% and 89% for each of the two monkeys, indicating a robust, decodable grasp pattern encoded in the PMd. Grasp decoding using LFPs, especially the high-frequency bands, also produced high decoding accuracies. Significance. This study is the first to specify the neuronal population encoding of grasp during the time course of grasp. We demonstrate high grasp decoding performance in PMd. These findings, combined with previous evidence for reach related modulation studies, suggest that PMd may play an important role in generation and maintenance of grasp action and may be a suitable locus for brain-machine interface applications.

Recently, local field potentials (LFPs) have been successfully used to extract information of arm and hand movement in some brain-machine interfaces (BMIs) studies, which suggested that LFPs would improve the performance of BMI applications because of its long-term stability. However, the performance of LFPs in different frequency bands has not been investigated in decoding hand grasp types. Here, the LFPs from the monkey's dorsal premotor cortices were collected by microelectrode array when monkey was performing grip-specific grasp task. A K-nearest neighbor classifier performed on the power spectrum of LFPs was used to decode grasping movements. The decoding powers of LFPs in different frequency bands, channels and trials used for training were also studied. The results show that the broad high frequency band (200–400Hz) LFPs achieved the best performance with classification accuracy reaching over 0.9. It infers that high frequency band LFPs in PMd cortex could be a promising source of control signals in developing functional BMIs for hand grasping.

Online decoding of grasp gestures and real-time control of a prosthetic hand using signals from dorsal premotor (PMd) and primary motor cortex (M1) is of fundamental importance in order to restore grasping capabilities in paralysed subjects. To this ultimate aim, authors of this study are addressing the problem of decoding signals from M1 to control an external device. In particular, this paper presents preliminary results of a study in which four different grasp types and the rest state have been successfully decoded from the brain signals of two trained monkeys while they were grasping different shaped objects. An artificial hand replicating the monkeys movements has been online controlled through the neural activity measured. Our results enabled asynchronous neural control of prosthetic hand gestures, which underline a feasible hand neural prosthesis in BMIs.

Local field potentials (LFP) are valuable signals for decoding motor kinematics in brain machine interfaces (BMIs). To take full advantage of LFPs, however, more systematic investigation of the relationship between LFPs and ipsilateral limb movement is required. In this paper we investigated the decoding power of LFPs for the ipsilateral wrist movement from two monkeys performing a 2D center-out task. The results show that LFPs could predict the ipsilateral wrist position and velocity with high accuracy, which is comparable to the accuracy of decoding the contralateral kinematics. Furthermore, similar to contralateral decoding, the low (0.3-5 Hz, 5-15 Hz) and high (100-200 Hz, 200-400 Hz) frequency bands resulted in significantly better decoding performance than the medium frequency bands. These results suggest that ipsilateral LFPs could be used to build better BMIs in similar ways of using contralateral LFPs.

Real-time computation, portability and flexibility are crucial for practical brain-machine interface (BMI) applications. In this work, we proposed Hardware Processing Modules (HPMs) as a method for accelerating BMI computation. Two HPMs have been developed. One is the field-programmable gate array (FPGA) implementation of spike sorting based on probabilistic neural network (PNN), and the other is the FPGA implementation of neural ensemble decoding based on Kalman filter (KF). These two modules were configured under the same framework and tested with real data from motor cortex recording in rats performing a lever-pressing task for water rewards. Due to the parallelism feature of FPGA, the computation time was reduced by several dozen times, while the results are almost the same as those from Matlab implementations. Such HPMs provide a high performance coprocessor for neural signal computation.

This paper presents an insect-machine interface for controlling the flight behavior of tethered honeybees. Flight initiation and cessation can be reproducibly generated using electrical pulses between two wire electrodes implanted into the honeybee's brain. Experiments are conducted to compare the effect of different stimulus patterns on the honeybee's behavior by parameters including success rate, response time and flight duration. The preliminary results enable us to carry out further research works on the control of complex flight behaviors or the neural mechanism of insect flight.

Probabilistic neural network (PNN), a kind of radial basis networks, is usually used for classification problems. It has the advantages of much faster training process and more accurate results using the minimum Bayesian risk criterion compared with other neural networks. In this paper, we use this neural network in brain-machine interface for decoding neural ensemble activity. Rats were trained to perform a lever pressing task for water rewards and implanted a chronic 16-channel microelectrode array in the primary motor cortex. The multi-channel neural activity and pressure signal of the lever was recorded simultaneously. In the PNN architecture, input vector was modified and contained not only current neuronal activity but also previously estimated pressure value which was taken as one input. This modified PNN was implemented in Matlab and obtained a good performance. Furthermore, field-programmable gate array (FPGA) based hardware implementation of PNN were developed and tested with real data. Because of the parallel computation ability and optimized architecture of FPGA, the results are as accurate as the realization of Matlab-based but the running speed is much faster. This indicates that the performance of current FPGA is competent for portable BMI applications.

In this paper, rats were trained to press a lever over a threshold to get water as rewards, and neural ensemble activities in primary motor cortex (MI) and pressure signal of the lever were recorded synchronously. Meanwhile, two algorithms, Kalman filter (KF) and Optimal Linear Estimation (OLE), were used to decode neural ensemble activities around the pressing events. After training, the pressure values were extracted from the neural signals by those decoders, and the performances were compared by Correlation Coefficient (CC) and Mean Square Error (MSE) with pressure values recorded from the lever. KF was better than OLE when the bin size 0.7, but it was not applicable in this experiment when the bin size >100ms for excessively smoothed. And OLE performs better when bin size >100ms for diminishing the randomicity of neural firing, which might imply that KF is more applicable in real-time system.

A practical brain-machine interface (BMI) requires real-time decoding algorithms to be realised in a portable device rather than a personal computer. In this article, a field-programmable gate array (FPGA) implementation of a probabilistic neural network (PNN) is proposed and developed to decode motor cortical ensemble recordings in rats performing a lever-pressing task for water rewards. A chronic 16-channel microelectrode array was implanted into the primary motor cortex of the rat to record neural activity, and the pressure signal of the lever were recorded simultaneously. To decode the pressure value from neural activity, both Matlab-based and FPGA-based mapping algorithms using a PNN were implemented and evaluated. In the FPGA architecture, training data of the network were stored in random access memory (RAM) blocks and multiply-add operations were realised by on-chip DSP48E slices. In the approximation of the activation function, a Taylor series and a look-up table (LUT) are used to achieve an accurate approximation. The results of FPGA implementation are as accurate as the realisation of Matlab, but the running speed is 37.9 times faster. This novel and feasible method indicates that the performance of current FPGAs is competent for portable BMI applications.

Human-Computer Interface (HCI) has become an important area of research and development for the disabled. A portable wireless eye movement-controlled Human-Computer Interface which can be used for the disabled who have motor paralysis and who cannot speak in multiple applications (such as communication aid and smart home applications) is described here. This Interface consists of four major parts: (1) surface electrodes, (2) a two-channel amplifier, (3) a laptop (or a micro-processor), and (4) a ZigBee wireless module. Horizontal and vertical Electro-Oculography (EOG) signals are measured using five surface electrodes placed on the head .The vertical electrodes are placed about 1.0 cm above the right eyebrow and 2.0 cm below the lower lid of the right eye, the horizontal electrodes are placed 2.0 cm lateral to the each side of outer canthi and the last electrode is placed on user's forehead to serve as a ground. The two-channel amplifier is comprised of instrumentation amplifiers, band-pass filters and shift circuits. The EOG signals are sampled at the rate of 250Hz and then sent to a laptop or a micro-processor for signal processing which is based on the method of mathematical morphology to recognize the direction of eye movements and voluntary eye blink. The ZigBee wireless communication technology, which is proved to be reliable, low-power and cost-efficient, is used in the portable interface. The subjects can control the wireless device or move a cursor over a screen by using this interface. The delay of this interface is less than 0.5s and errors are very limited. This interface provides a flexible method for the disabled to improve the life quality.

Objective. The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task. Approach. Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons. Main results. The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10–20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset. Significance. The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different decoding models. It shows better robustness of identifying the important neurons with noisy signals presented. The low demand of computational resources which, reflected by the fast convergence, indicates the feasibility of the method applied in portable BMI systems. The ascertainment of the important neurons helps to inspect neural patterns visually associated with the movement task. The elimination of irrelevant neurons greatly reduces the computational burden of mBMI systems and maintains the performance with better robustness.