Journal of neural engineering最新文献

{"title":"Annotating neurophysiologic data at scale with optimized human input.","authors":"Zhongchuan Xu, Brittany H Scheid, Erin C Conrad, Kathryn A Davis, Taneeta Ganguly, Michael A Gelfand, James J Gugger, Xiangyu Jiang, Joshua J LaRocque, William K S Ojemann, Saurabh R Sinha, Genna J Waldman, Joost Wagenaar, Nishant Sinha, Brian Litt","doi":"10.1088/1741-2552/ade402","DOIUrl":"10.1088/1741-2552/ade402","url":null,"abstract":"<p><p><i>Objective.</i>Neuroscience experiments and devices are generating unprecedented volumes of data, but analyzing and validating them presents practical challenges, particularly in annotation. While expert annotation remains the gold standard, it is time consuming to obtain and often poorly reproducible. Although automated annotation approaches exist, they rely on labeled data first to train machine learning algorithms, which limits their scalability. A semi-automated annotation approach that integrates human expertise while optimizing efficiency at scale is critically needed. To address this, we present Annotation Co-pilot, a human-in-the-loop solution that leverages deep active learning (AL) and self-supervised learning (SSL) to improve intracranial EEG (iEEG) annotation, significantly reducing the amount of human annotations.<i>Approach.</i>We automatically annotated iEEG recordings from 28 humans and 4 dogs with epilepsy implanted with two neurodevices that telemetered data to the cloud for analysis. We processed 1500 h of unlabeled iEEG recordings to train a deep neural network using a SSL method Swapping Assignments between View to generate robust, dataset-specific feature embeddings for the purpose of seizure detection. AL was used to select only the most informative data epochs for expert review. We benchmarked this strategy against standard methods.<i>Main result.</i>Over 80 000 iEEG clips, totaling 1176 h of recordings were analyzed. The algorithm matched the best published seizure detectors on two datasets (NeuroVista and NeuroPace responsive neurostimulation) but required, on average, only 1/6 of the human annotations to achieve similar accuracy (area under the ROC curve of 0.9628 ± 0.015) and demonstrated better consistency than human annotators (Cohen's Kappa of 0.95 ± 0.04).<i>Significance</i>. 'Annotation Co-pilot' demonstrated expert-level performance, robustness, and generalizability across two disparate iEEG datasets while reducing annotation time by an average of 83%. This method holds great promise for accelerating basic and translational research in electrophysiology, and potentially accelerating the pathway to clinical translation for AI-based algorithms and devices.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12223546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry of orofacial neuromuscular signals: speech articulation decoding using surface electromyography.","authors":"Harshavardhana T Gowda, Zachary D McNaughton, Lee M Miller","doi":"10.1088/1741-2552/ade7af","DOIUrl":"10.1088/1741-2552/ade7af","url":null,"abstract":"<p><p><i>Objective.</i>In this article, we present data and methods for decoding speech articulations using surface electromyogram (EMG) signals. EMG-based speech neuroprostheses offer a promising approach for restoring audible speech in individuals who have lost the ability to speak intelligibly due to laryngectomy, neuromuscular diseases, stroke, or trauma-induced damage (e.g. from radiotherapy) to the speech articulators.<i>Approach.</i>To achieve this, we collect EMG signals from the face, jaw, and neck as subjects articulate speech, and we perform EMG-to-speech translation.<i>Main results.</i>Our findings reveal that the manifold of symmetric positive definite matrices serves as a natural embedding space for EMG signals. Specifically, we provide an algebraic interpretation of the manifold-valued EMG data using linear transformations, and we analyze and quantify distribution shifts in EMG signals across individuals.<i>Significance.</i>Overall, our approach demonstrates significant potential for developing neural networks that are both data- and parameter-efficient-an important consideration for EMG-based systems, which face challenges in large-scale data collection and operate under limited computational resources on embedded devices.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-stage EEG zero-shot classification algorithm guided by class reconstruction.","authors":"Li Li, Baofa Wei","doi":"10.1088/1741-2552/adeaea","DOIUrl":"https://doi.org/10.1088/1741-2552/adeaea","url":null,"abstract":"<p><p>Researchers have long been dedicated to decoding human visual representations from neural signals. These studies are crucial in uncovering the mechanisms of visual processing in the human brain. Electroencephalogram(EEG) signals have garnered widespread attention recently due to their noninvasive nature and low cost. EEG classification is one of the most popular topics in brain-computer interface(BCI) research. However, most traditional EEG classification algorithms are difficult to generalize to unseen classes that were not involved in the training phase. The main objective of this work is to improve the performance of these EEG classification algorithms for unseen classes. In this work, we propose a two-stage zero-shot EEG classification algorithm guided by class reconstruction. The method is specifically designed with a two-stage training strategy based on class reconstruction. This structure and training strategy enable the model to thoroughly learn the relations and distinctions among EEG embeddings of different classes. The Contrastive Language-Image Pre-training(CLIP) model has a well-aligned latent space and powerful cross-modality generalization ability. The method bridges the modality gap between EEG, images, and text using CLIP features. It significantly improves the model's performance in unseen classes. We conducted experiments on the ImageStimulus-EEG dataset to evaluate the performance of the proposed method. Meanwhile, it was compared with the state-of-the-art model and the baseline model. The experimental results demonstrate that our model achieves superior performance in among Top-1, Top-3, and Top-5 classification accuracy for a 50-way zero-shot classification task, reaching 17.77%, 38.76% and 54.75%, respectively. These results further validate the effectiveness of the proposed method in EEG zero-shot classification.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust neural prosthetic control strategy against arm position variability and fatigue based on multi-sensor fusion.","authors":"Shang Shi, Jianjun Meng, Zongtian Yin, Weichao Guo, Xiangyang Zhu","doi":"10.1088/1741-2552/ade504","DOIUrl":"10.1088/1741-2552/ade504","url":null,"abstract":"<p><p><i>Objective</i>. Multi-modal sensor fusion comprising surface electromyography (sEMG) and A-mode ultrasound (US) has yielded satisfactory performance in gesture recognition, aiding amputees in restoring upper limb function. However, prior research conducted in laboratory settings with consistent arm positions lacks practical application for amputees using prostheses. Additionally, motion tests utilized in current studies necessitate prolonged gesture execution, while constant muscle contractions introduce fatigue and increase misclassification risk in practical applications. Consequently, implementing a robust control is imperative to mitigate the limitations of constant arm positions and muscle contractions.<i>Approach</i>. This paper introduces a novel decoding strategy for online applications based on A-mode US, sEMG, and inertial movement unit (IMU) sensor fusion. The decoding process comprises four stages: arm position selection, sEMG threshold, pattern recognition, and a post-processing strategy, which preserves the previous short-duration hand gesture during rest and aims to improve prosthetic hand control performance for practical applications.<i>Main results</i>. The offline classification accuracy achieves 96.02% based on fusion sensor decoding. It drops to 90.72% for healthy participants when wearing an arm fixture that simulates the load of a real prosthesis. The implementation of the post-processing strategy results in a 92.51% online classification accuracy (ONCA) for recognized gestures in three varied arm positions, significantly higher than the 78.97% ONCA achieved when the post-processing strategy is disabled.<i>Significance</i>. The post-processing strategy mitigates constant muscle contraction, demonstrating high robustness to prosthetic hand control. The proposed online decoding strategy achieves remarkable performance on customized prostheses for two amputees across various arm positions, providing a promising prospect for multi-modal sensor fusion based prosthetic applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contralesional recruitment and localization of EEG signal complexity in stroke: a recurrence quantification analysis of hierarchical motor tasks.","authors":"Parikshat Sirpal, Nishaal Parmar, Hazem H Refai, Julius P A Dewald, Yuan Yang","doi":"10.1088/1741-2552/ade6ab","DOIUrl":"10.1088/1741-2552/ade6ab","url":null,"abstract":"<p><p><i>Objective.</i>Effective characterization of neural complexity during motor execution tasks enhances understanding of maladaptive cortical reorganization in stroke and inform targeted rehabilitation. While traditional EEG analyses often do not consider nonlinear temporal dynamics, we introduce a recurrence based computational framework to quantify cortical complexity during hierarchical motor tasks. Here, we evaluate contralesional motor system engagement in stroke survivors using recurrence quantification analysis (RQA), ensuring sensitivity to nonlinear and temporally structured cortical activity.<i>Approach</i>. RQA was applied to EEG signals recorded during shoulder abduction (SABD) at 20% and 40% torque levels to characterize nonlinear cortical dynamics and quantify complexity distinguishing adaptive from maladaptive motor system engagement. Spatially resolved recurrence metrics were compared between stroke and control participants to elucidate compensatory cortical reorganization linked to motor impairment and hierarchical task demands.<i>Results</i>. Our findings show a statistically significant increase in EEG signal complexity within the contralesional hemisphere of stroke participants, particularly under higher SABD loads. Consistent with previous studies, we observed abnormal muscle coactivation patterns between proximal and distal muscles, along with distinct shifts in EMG vector direction in stroke-impaired limbs. These shifts in coactivation patterns suggest constraints in muscle coactivation patterns resulting from losses in corticofugal projections and upregulated brainstem pathways.<i>Significance</i>. We introduce a novel application of RQA to quantify nonlinear EEG complexity during motor execution in chronic stroke. Our results show that increased EEG complexity reflects greater recruitment of contralesional motor pathways, indicating maladaptive cortical reorganization linked to impaired motor control. Unlike traditional spectral or connectivity-based EEG signal processing methods, RQA quantifies temporally evolving, nonlinear recurrence dynamics, serving as a marker of maladaptive contralesional motor recruitment, positioning RQA as a promising, clinically meaningful, and computationally efficient tool to evaluate cortical dynamics and guide targeted neurorehabilitation strategies aimed at minimizing maladaptive plasticity.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.","authors":"Dongrui Wu","doi":"10.1088/1741-2552/addd49","DOIUrl":"10.1088/1741-2552/addd49","url":null,"abstract":"<p><p>Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel paradigm for two-degree-of-freedom BCI control based on ERP in-duced by overt and covert visual attention.","authors":"Hailing Xin, Hairong Li, Hongzhi Qi","doi":"10.1088/1741-2552/ade56a","DOIUrl":"10.1088/1741-2552/ade56a","url":null,"abstract":"<p><p><i>Objective.</i>This study developed a novel brain-computer interface (BCI) paradigm based on event-related potentials (ERPs) to achieve simultaneous two-degree-of-freedom control through overt and covert visual selective attention.<i>Approach.</i>In this paradigm, three stimuli were arranged equidistantly around the cursor. Participants selected two stimuli as attention targets based on the relative position of the cursor and the intended movement destination, focusing overtly on one while covertly attending to the other. EEG data collected during offline experiments were used to train classifiers for overt and covert targets (CT), and the outputs of these classifiers were employed in online experiments to construct movement vectors for controlling the cursor in a 2D space.<i>Main results.</i>EEG analysis demonstrated that overt and CT elicited distinct ERP signals, with classification accuracies of 96.2% and 92.4%, respectively. The accuracy of simultaneously identifying both targets reached 91.0%. In online experiments, the success rate of moving the cursor to the target region was 92.6%, and 88.2% of cursor movements were in the desired direction. These results confirm the feasibility of achieving 2D control through ERP based selective attention and validate the effectiveness of the proposed paradigm.<i>Significance.</i>This study introduces a novel EEG-based approach for multi-degree-of-freedom control, expanding the capabilities of traditional ERP based BCIs, which have primarily been limited to single-degree-of-freedom applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-based affective brain-computer interfaces: recent advancements and future challenges.","authors":"Yuxin Chen, Yong Peng, Jiajia Tang, Tracey Camilleri, Kenneth Camilleri, Wanzeng Kong, Andrzej Cichocki","doi":"10.1088/1741-2552/ade290","DOIUrl":"10.1088/1741-2552/ade290","url":null,"abstract":"<p><p><i>Objective</i>. As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e. depression, anxiety) are detected, which are considered as the two basic functions of aBCI systems. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems.<i>Approach</i>. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders.<i>Main results</i>. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information', 'outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI.<i>Significance</i>. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI systems in practical deployment.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.","authors":"Hisham Temmar, Matthew S Willsey, Joseph T Costello, Matthew J Mender, Luis Hernan Cubillos, Jesse C DeMatteo, Jordan Lw Lam, Dylan M Wallace, Madison M Kelberman, Parag G Patil, Cynthia A Chestek","doi":"10.1088/1741-2552/ade568","DOIUrl":"10.1088/1741-2552/ade568","url":null,"abstract":"<p><p><i>Objective.</i>Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how nonlinear and linear approaches predict individuated finger movements in open and closed-loop settings.<i>Approach.</i>Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex and performed a 2D dexterous finger movement task for a juice reward. Multiple linear and nonlinear 'decoders' were used to map from recorded spiking band power into movement kinematics. Performance of these decoders was compared and analyzed to determine how nonlinear decoders perform in both open and closed-loop scenarios.<i>Main Results.</i>We show that nonlinear decoders enable control which more closely resembles true hand movements, producing distributions of velocities 80.7% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of temporally-convolved feedforward neural network convergence by up to 188.9%, along with improving average performance and training speed. Finally, we show that TCNs and long short-term memory can effectively leverage training data from multiple task variations to improve generalization.<i>Significance.</i>The results of this study support artificial neural networks of all kinds as the future of BMI decoding and show potential for generalizing over less constrained tasks.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optogenetic neural spheroids excite primary neural network.","authors":"Saeedur Rahman, Md Saddam Hossain Joy, M Taher A Saif","doi":"10.1088/1741-2552/ade28d","DOIUrl":"10.1088/1741-2552/ade28d","url":null,"abstract":"<p><p><i>Objective.</i>Optical stimulation of<i>in vitro</i>neurons requires prior transfection with light gated ion channels. This additional step brings complexity and requires optimization. Simplification of the process will ease the undertaking of studies on biological neural networks needing external stimulation.<i>Approach.</i>We constructed a simple platform where embryonic stem cell derived optogenetic neural spheroids, cultured and maintained separately, can be seeded on top of the primary non-optogenetic neuron cultures.<i>Main results.</i>We found that the primary neural network can be stimulated through the spheroids. This allows making investigations like network response dynamics and pharmacological perturbations possible.<i>Significance.</i>Thus, our platform provides an on-demand method to stimulate neural preparations for many different studies.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}