Journal of neural engineering最新文献

{"title":"Personalizing brain stimulation: continual learning for sleep spindle detection.","authors":"Milo Sobral, Hugo R Jourde, Seyed Ehsan Marjani Bajestani, Emily B J Coffey, Giovanni Beltrame","doi":"10.1088/1741-2552/adebb1","DOIUrl":"https://doi.org/10.1088/1741-2552/adebb1","url":null,"abstract":"<p><p>Personalized closed-loop brain stimulation, in which algorithms used to detect neural events adapt to a user's unique neural characteristics, may be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications. Precise stimulation of sleep spindles-transient patterns of brain activity that occur during non rapid eye movement sleep that are involved in memory consolidation-presents an exciting frontier for studying memory functions; however, this endeavor is challenged by the spindles' fleeting nature, inter-individual variability, and the necessity of real-time detection. &#xD;Methods: This paper introduces an approach to tackle these challenges, centered around a novel continual learning framework. Using a pre-trained model capable of both online classification of sleep stages and spindle detection, we implement an algorithm that refines spindle detection, tailoring it to the individual throughout one or more nights without manual intervention. &#xD;Results: Our methodology achieves accurate, subject-specific targeting of sleep spindles and enables advanced closed-loop stimulation studies. &#xD;Conclusion: While fine-tuning alone offers minimal benefits for single nights, our approach combining weight averaging demonstrates significant improvement over multiple nights, effectively mitigating catastrophic forgetting. &#xD;Significance: This advancement represents a crucial step towards personalized closed-loop brain stimulation, potentially leading to a deeper understanding of sleep spindle functions and their role in memory consolidation. It holds the promise of deepening our understanding of sleep spindles' role in memory consolidation for cognitive neuroscience research and therapeutic applications.</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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562374","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":"Deep learning on high-density EEG during a cognitive task distinguishes patients with Parkinson's disease from healthy controls.","authors":"Yves Denoyer, Joan Duprez, Jean-François Houvenaghel, Fabrice Wendling, Pascal Benquet","doi":"10.1088/1741-2552/ade6a9","DOIUrl":"10.1088/1741-2552/ade6a9","url":null,"abstract":"<p><p><i>Objective.</i>Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive impairment. Its diagnosis, which used to be based on clinical assessment, increasingly relies on biomarkers. While electroencephalography (EEG) biomarkers are still at an experimental stage, they have been studied using deep learning (DL) models. Our aim was to determine whether a cognitive task could improve the accuracy of EEG-based disease detection by activating cortical regions affected by the disease.<i>Approach</i>. We trained a DL model to discriminate PD patients from controls based on their high-density EEG recordings. Previous studies have employed a range of preprocessing techniques, models and, predominantly, resting state (RS) EEG. We also investigated different network architectures and hyperparameters, and the role of spatial and temporal resolution.<i>Main results</i>. The best model gave a classification accuracy of 83% on the cognitive task EEG dataset and 76% on the RS EEG dataset. Sensitivity analysis indicated that the model predominantly uses specific temporal and spatial components of the EEG in the cognitive task condition, differing from the RS.<i>Significance</i>. Our results suggest that cortical activation by the cognitive task unveils EEG features that are effective in distinguishing between PD and controls. These features can be used by the model, thereby improving its diagnostic accuracy.</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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337386","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":"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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287673","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":"Detecting single motor-unit activity in magnetomyography.","authors":"Nima Noury, Justus Marquetand, Stefan Hartwig, Thomas Middelmann, Philip Broser, Markus Siegel","doi":"10.1088/1741-2552/adeaeb","DOIUrl":"https://doi.org/10.1088/1741-2552/adeaeb","url":null,"abstract":"<p><strong>Objective: </strong>The study of motor unit (MU) discharge patterns is critical for understanding the mechanisms underlying human motor behavior. Intramuscular electromyography (iEMG) allows direct study of MU activity but is invasive. Surface electromyography (sEMG) offers a non-invasive alternative, but with lower spatial resolution. Recent advances in optically pumped magnetometers (OPMs) have sparked interest in the magnetic counterpart of EMG, magnetomyography (MMG), as an additional non-contact modality to study the neuromuscular system. However, it remains unclear whether MMG signals recorded with superconducting quantum interference devices (SQUIDs) or OPMs can be used to directly detect individual MUs.</p><p><strong>Approach: </strong>We addressed this question in a proof-of-principle study in which we recorded MMG signals from the abductor digiti minimi (ADM) muscle using SQUIDs and OPMs. Critically, we simultaneously recorded iEMG from the same muscle as the ground truth to cross-validate the findings from the non-invasive measurements.</p><p><strong>Main results: </strong>We found that invasively recorded MUs can be detected in simultaneously recorded SQUID and OPM MMG signals. Furthermore, we found that individual MUs can be extracted directly from SQUID and OPM MMG and, importantly, validated this finding using the simultaneous iEMG recordings. These results provide converging evidence that individual MU activity is accessible using non-contact MMG.</p><p><strong>Significance: </strong>We demonstrate for the first time that individual MU activity is observable in MMG, paving the way for future research on MU decomposition using MMG. Our findings highlight the potential of MMG as a non-contact modality to study muscle activity in health and disease.</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":"144556293","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":"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 transformer-based network with second-order pooling for motor imagery EEG classification.","authors":"Jing Jin, Wei Liang, Ren Xu, Weijie Chen, Ruitian Xu, Xingyu Wang, Andrzej Cichocki","doi":"10.1088/1741-2552/adeae8","DOIUrl":"https://doi.org/10.1088/1741-2552/adeae8","url":null,"abstract":"<p><strong>Objective: </strong>Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning models have been developed to decode EEG signals. Although deep learning models, particularly those based on convolutional neural networks (CNNs), have shown promise in decoding EEG signals, most existing methods focus on attention mechanisms while neglecting high-order statistical dependencies that are critical for accurately capturing the complex structure of EEG data.</p><p><strong>Approach: </strong>To address this limitation, we propose a neural network integrating a transpose-attention mechanism and second-order pooling (SecTNet). The proposed model tackles two fundamental challenges in EEG decoding. It metrics the covariance structure of EEG signals using Riemannian geometry on symmetric positive definite (SPD) matrices, and it enhances the discriminability of these SPD features by introducing attention mechanisms that adaptively model inter-channel dependencies. Specifically, SecTNet is composed of three key components. First, a multi-scale spatial-temporal convolution module extracts detailed local features. Second, a transpose-attention mechanism captures dependency information from the internal interactions between channels. Lastly, a second-order pooling layer captures high-order statistical correlations in the EEG feature space.</p><p><strong>Main results: </strong>SecTNet is evaluated on two publicly available EEG datasets, namely BCI Competition IV 2a dataset and OpenBMI dataset. It achieves an average accuracy of 86.88% on the BCI Competition IV dataset 2a and 74.99% on the OpenBMI dataset. Moreover, results show that SecTNet maintains competitive performance even when trained on only 50% of the data, demonstrating strong generalization under limited data conditions.</p><p><strong>Significance: </strong>These results demonstrate the broad applicability and effectiveness of SecTNet in enhancing MI-BCI performance. SecTNet provides a robust and generalizable framework for EEG decoding, supporting the development of BCI applications across diverse real-world scenarios.</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":"144556290","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":"Bayesian optimization of cortical neuroprosthetic vision using perceptual feedback.","authors":"Burcu Küçükoğlu, Leili Soo, David Leeftink, Fabrizio Grani, Cristina Soto Sanchez, Umut Güçlü, Marcel A J van Gerven, Eduardo Fernandez","doi":"10.1088/1741-2552/adeae9","DOIUrl":"https://doi.org/10.1088/1741-2552/adeae9","url":null,"abstract":"<p><strong>Objective: </strong>The challenge in cortical neuroprosthetic vision is determining the optimal, safe stimulation patterns to evoke the desired light perceptions (`phosphenes') in blind individuals. Clinical studies gain insights into the perceptual characteristics of phosphenes through patient descriptions on provided stimulation protocols. However, the huge parameter space for multi-electrode stimulation makes it difficult to identify the optimality of the stimulation that lead to well-perceived phosphenes. A systematic search in the parameter space of the electrical stimulation is needed to achieve good perception. Bayesian optimization (BO) is a framework for finding optimal parameters efficiently. Using patient's perceptual feedback, a model of patient response based on iteratively generated stimulation protocols can be built to maximize perception quality.&#xD;Approach. A patient implanted with an intracortical 96-channel microelectrode array in their visual cortex was iteratively presented with stimulation protocols, generated via BO vs. random generation (RG) in two separate experiments. Whereas standard BO methods do not scale well to problems with over a dozen inputs, we optimize a set of 40 electrode currents using trust region-based BO. The protocols determine the electrodes to stimulate and with how much current (0-50 μA), on a total current limit of 500 μA. The patient rated each stimulation's perceptual quality on a Likert scale, where 7 indicated the highest quality and 0 no perception. &#xD;Main results. The patient ratings gradually converged on higher values with BO, compared to the RG experiment. BO gradually generated protocols with higher total current, in line with the patient preference for higher currents due to brighter phosphenes. Electrodes previously observed as effective in producing phosphene perception were chosen more by BO also with higher current allocation.&#xD;Significance. This study demonstrates the power of BO in converging to optimal stimulation protocols based on patient feedback, providing an efficient search for stimulation parameters for clinical studies.NCT02983370.</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":"144556292","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":"Sub-scalp EEG for sensorimotor brain-computer interface.","authors":"Tim B Mahoney, David B Grayden, Sam E John","doi":"10.1088/1741-2552/ade9f1","DOIUrl":"https://doi.org/10.1088/1741-2552/ade9f1","url":null,"abstract":"<p><strong>Objective: </strong>To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity.&#xD;Approach: Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment. &#xD;Main Results: We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models. &#xD;Significance: These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.</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":"144532060","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}