Cognitive Neurodynamics最新文献

{"title":"Classification of coma/brain-death EEG dataset based on one-dimensional convolutional neural network.","authors":"Boning Li, Jianting Cao","doi":"10.1007/s11571-023-09942-2","DOIUrl":"10.1007/s11571-023-09942-2","url":null,"abstract":"<p><p>Electroencephalography (EEG) evaluation is an important step in the clinical diagnosis of brain death during the standard clinical procedure. The processing of the brain-death EEG signals acquisition always carried out in the Intensive Care Unit (ICU). The electromagnetic environmental noise and prescribed sedative may erroneously suggest cerebral electrical activity, thus effecting the presentation of EEG signals. In order to accurately and efficiently assist physicians in making correct judgments, this paper presents a band-pass filter and threshold rejection-based EEG signal pre-processing method and an EEG-based coma/brain-death classification system associated with One Dimensional Convolutional Neural Network (1D-CNN) model to classify informative brain activity features from real-world recorded clinical EEG data. The experimental result shows that our method is well performed in classify the coma patients and brain-death patients with the classification accuracy of 99.71%, F1-score of 99.71% and recall score of 99.51%, which means the proposed model is well performed in the coma/brain-death EEG signals classification task. This paper provides a more straightforward and effective method for pre-processing and classifying EEG signals from coma/brain-death patients, and demonstrates the validity and reliability of the method. Considering the specificity of the condition and the complexity of the EEG acquisition environment, it presents an effective method for pre-processing real-time EEG signals in clinical diagnoses and aiding the physicians in their diagnosis, with significant implications for the choice of signal pre-processing methods in the construction of practical brain-death identification systems.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49584483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine to brain: facial expression recognition using brain machine generative adversarial networks.","authors":"Dongjun Liu, Jin Cui, Zeyu Pan, Hangkui Zhang, Jianting Cao, Wanzeng Kong","doi":"10.1007/s11571-023-09946-y","DOIUrl":"10.1007/s11571-023-09946-y","url":null,"abstract":"<p><p>The human brain can effectively perform Facial Expression Recognition (FER) with a few samples by utilizing its cognitive ability. However, unlike the human brain, even the well-trained deep neural network is data-dependent and lacks cognitive ability. To tackle this challenge, this paper proposes a novel framework, Brain Machine Generative Adversarial Networks (BM-GAN), which utilizes the concept of brain's cognitive ability to guide a Convolutional Neural Network to generate LIKE-electroencephalograph (EEG) features. More specifically, we firstly obtain EEG signals triggered from facial emotion images, then we adopt BM-GAN to carry out the mutual generation of image visual features and EEG cognitive features. BM-GAN intends to use the cognitive knowledge learnt from EEG signals to instruct the model to perceive LIKE-EEG features. Thereby, BM-GAN has a superior performance for FER like the human brain. The proposed model consists of VisualNet, EEGNet, and BM-GAN. More specifically, VisualNet can obtain image visual features from facial emotion images and EEGNet can obtain EEG cognitive features from EEG signals. Subsequently, the BM-GAN completes the mutual generation of image visual features and EEG cognitive features. Finally, the predicted LIKE-EEG features of test images are used for FER. After learning, without the participation of the EEG signals, an average classification accuracy of 96.6 % is obtained on Chinese Facial Affective Picture System dataset using LIKE-EEG features for FER. Experiments demonstrate that the proposed method can produce an excellent performance for FER.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46929635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data.","authors":"Esra Kaya, Ismail Saritas","doi":"10.1007/s11571-023-09957-9","DOIUrl":"10.1007/s11571-023-09957-9","url":null,"abstract":"<p><p>The concept of the brain-computer interface (BCI) has become one of the popular research topics of recent times because it allows people to express their thoughts and control different applications and devices without actual movement. The communication between the brain and the computer or a machine is generally provided through Electroencephalogram (EEG) signals because they are cost-effective and easy to implement in normal life, not just in healthcare facilities. On the other hand, they are hard to process efficiently due to their nonlinearity and noisy nature. Thus, the field of BCI and EEG needs constant work and improvement. This paper focuses on generalizing the most efficient EEG channels and the most significant features of motor imagery (MI) signals by analyzing the recordings of one participant obtained over 20 different days. Because the classification performance usually decreases with an increasing number of class labels, we have realized the study by analyzing the signals through a new paradigm consisting of multi-class directional labels: right, left, forward, and backward. Afterward, the results are tested on EEG data obtained from 5 participants to see if the results are consistent with each other. The average accuracy of binary and multi-class classification using the Ensemble Subspace Discriminant classifier was found as 87.39 and 61.44%, respectively, with the most efficient 3-channel combination for daily BCI evaluation of one participant. On the other hand, the average accuracy of binary and multi-class classification was found as 71.84 and 50.42%, respectively, for 5 participants, with the most efficient channel combination of 4, where the first three are the same as the daily performance of one participant. During signal processing, the outliers of the signals were discarded by considering the channels separately. An algorithm was developed to dismiss the inconsistent samples within the classes. A novel adaptive filtering approach, correlation-based adaptive variational mode decomposition (CBAVMD), was proposed. The feature selection was realized based on the standard deviation values of the features between the classes. The paradigm based on the direction movements was found to be most effective, especially for binary classification of right and left directions. The generalization of effective channels and features was found to be generally successful.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46869982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic causal modeling analysis reveals the modulation of motor cortex and integration in superior temporal gyrus during multisensory speech perception.","authors":"Ting Zou, Liyuan Li, Xinju Huang, Chijun Deng, Xuyang Wang, Qing Gao, Huafu Chen, Rong Li","doi":"10.1007/s11571-023-09945-z","DOIUrl":"10.1007/s11571-023-09945-z","url":null,"abstract":"<p><p>The processing of speech information from various sensory modalities is crucial for human communication. Both left posterior superior temporal gyrus (pSTG) and motor cortex importantly involve in the multisensory speech perception. However, the dynamic integration of primary sensory regions to pSTG and the motor cortex remain unclear. Here, we implemented a behavioral experiment of classical McGurk effect paradigm and acquired the task functional magnetic resonance imaging (fMRI) data during synchronized audiovisual syllabic perception from 63 normal adults. We conducted dynamic causal modeling (DCM) analysis to explore the cross-modal interactions among the left pSTG, left precentral gyrus (PrG), left middle superior temporal gyrus (mSTG), and left fusiform gyrus (FuG). Bayesian model selection favored a winning model that included modulations of connections to PrG (mSTG → PrG, FuG → PrG), from PrG (PrG → mSTG, PrG → FuG), and to pSTG (mSTG → pSTG, FuG → pSTG). Moreover, the coupling strength of the above connections correlated with behavioral McGurk susceptibility. In addition, significant differences were found in the coupling strength of these connections between strong and weak McGurk perceivers. Strong perceivers modulated less inhibitory visual influence, allowed less excitatory auditory information flowing into PrG, but integrated more audiovisual information in pSTG. Taken together, our findings show that the PrG and pSTG interact dynamically with primary cortices during audiovisual speech, and support the motor cortex plays a specifically functional role in modulating the gain and salience between auditory and visual modalities.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-023-09945-z.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43947873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback.","authors":"Linling Li, Yutong Li, Zhaoxun Li, Gan Huang, Zhen Liang, Li Zhang, Feng Wan, Manjun Shen, Xue Han, Zhiguo Zhang","doi":"10.1007/s11571-023-09939-x","DOIUrl":"10.1007/s11571-023-09939-x","url":null,"abstract":"<p><p>EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional features. However, they only focused on regional brain structure or function and overlooked possible neural correlates of the brain network. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. We designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data from healthy participants before training. We assessed the learning performance for evoked EEG modulations during training (L1) and at rest (L2), and investigated performance-related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. The main findings of this study are described below. First, both real and sham groups could increase their FAA during training, but only the real group showed a significant increase in FAA at rest. Second, the predictors during training blocks and at rests were different: L1 was correlated with the graph-theoretical metrics (clustering coefficient and local efficiency) of the right hemispheric gray matter and functional networks, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left the hemispheric functional network. Therefore, the individual differences in FAA neurofeedback learning could be explained by individual variations in structural/functional architecture, and the correlated graph-theoretical metrics of learning performance indices showed different laterality of hemispheric networks. These results provided insight into the neural correlates of inter-individual differences in neurofeedback learning.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-023-09939-x.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46010742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectual seizure detection using MBBF-GPSO with CNN network.","authors":"Dinesh Kumar Atal, Mukhtiar Singh","doi":"10.1007/s11571-023-09943-1","DOIUrl":"10.1007/s11571-023-09943-1","url":null,"abstract":"<p><p>EEG is the most common test for diagnosing a seizure, where it presents information about the electrical activity of the brain. Automatic Seizure detection is one of the challenging tasks due to limitations of conventional methods with regard to inefficient feature selection, increased computational complexity and time and less accuracy. The situation calls for a practical framework to achieve better performance for detecting the seizure effectively. Hence, this study proposes modified Blackman bandpass filter-greedy particle swarm optimization (MBBF-GPSO) with convolutional neural network (CNN) for effective seizure detection. In this case, unwanted signals (noise) is eliminated by MBBF as it possess better ability in stopband attenuation, and, only the optimized features are selected using GPSO. For enhancing the efficacy of obtaining optimal solutions in GPSO, the time and frequency domain is extracted to complement it. Through this process, an optimized features are attained by MBBF-GPSO. Then, the CNN layer is employed for obtaining the productive classification output using the objective function. Here, CNN is employed due to its ability in automatically learning distinct features for individual class. Such advantages of the proposed system have made it explore better performance in seizure detection that is confirmed through performance and comparative analysis.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45996684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain.","authors":"Cheng-Li Zhao, Wenjie Hou, Yanbing Jia, Barbara J Sahakian, Qiang Luo","doi":"10.1007/s11571-023-09954-y","DOIUrl":"10.1007/s11571-023-09954-y","url":null,"abstract":"<p><p>Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-023-09954-y.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46022889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of neurodegenerative diseases with a refined Lempel-Ziv complexity.","authors":"Huan Zhao, Junxiao Xie, Yangquan Chen, Junyi Cao, Wei-Hsin Liao, Hongmei Cao","doi":"10.1007/s11571-023-09973-9","DOIUrl":"10.1007/s11571-023-09973-9","url":null,"abstract":"<p><p>The investigation into the distinctive difference of gait is of significance for the clinical diagnosis of neurodegenerative diseases. However, human gait is affected by many factors like behavior, occupation and so on, and they may confuse the gait differences among Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. For the purpose of examining distinctive gait differences of neurodegenerative diseases, this study extracts various features from both vertical ground reaction force and time intervals. Moreover, refined Lempel-Ziv complexity is proposed considering the detailed distribution of signals based on the median and quartiles. Basic features (mean, coefficient of variance, and the asymmetry index), nonlinear dynamic features (Hurst exponent, correlation dimension, largest Lyapunov exponent), and refined Lempel-Ziv complexity of different neurodegenerative diseases are compared statistically by violin plot and Kruskal-Wallis test to reveal distinction and regularities. The comparative analysis results illustrate the gait differences across these neurodegenerative diseases by basic features and nonlinear dynamic features. Classification results by random forest indicate that the refined Lempel-Ziv complexity can robustly enhance the diagnosis accuracy when combined with basic features.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49490536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-length SSVEP data extension by a novel generative adversarial networks based framework","authors":"Yudong Pan, Ning Li, Yangsong Zhang, Peng Xu, Dezhong Yao","doi":"10.1007/s11571-024-10134-9","DOIUrl":"https://doi.org/10.1007/s11571-024-10134-9","url":null,"abstract":"<p>Steady-state visual evoked potentials (SSVEPs) based brain–computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create synthetic electroencephalography data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class and 12-class dataset). With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Break-up and recovery of harmony between direct and indirect pathways in the basal ganglia: Huntington’s disease and treatment","authors":"Sang-Yoon Kim, Woochang Lim","doi":"10.1007/s11571-024-10125-w","DOIUrl":"https://doi.org/10.1007/s11571-024-10125-w","url":null,"abstract":"<p>The basal ganglia (BG) in the brain exhibit diverse functions for motor, cognition, and emotion. Such BG functions could be made via competitive harmony between the two competing pathways, direct pathway (DP) (facilitating movement) and indirect pathway (IP) (suppressing movement). As a result of break-up of harmony between DP and IP, there appear pathological states with disorder for movement, cognition, and psychiatry. In this paper, we are concerned about the Huntington’s disease (HD), which is a genetic neurodegenerative disorder causing involuntary movement and severe cognitive and psychiatric symptoms. For the HD, the number of D2 SPNs (<span>(N_{rm D2})</span>) is decreased due to degenerative loss, and hence, by decreasing <span>(x_{rm D2})</span> (fraction of <span>(N_{rm D2})</span>), we investigate break-up of harmony between DP and IP in terms of their competition degree <span>(mathcal{C}_d)</span>, given by the ratio of strength of DP (<span>(mathcal{S}_{DP})</span>) to strength of IP (<span>(mathcal{S}_{IP})</span>) (i.e., <span>(mathcal{C}_d = mathcal{S}_{DP} / mathcal{S}_{IP})</span>). In the case of HD, the IP is under-active, in contrast to the case of Parkinson’s disease with over-active IP, which results in increase in <span>(mathcal{C}_d)</span> (from the normal value). Thus, hyperkinetic dyskinesia such as chorea (involuntary jerky movement) occurs. We also investigate treatment of HD, based on optogenetics and GP ablation, by increasing strength of IP, resulting in recovery of harmony between DP and IP. Finally, we study effect of loss of healthy synapses of all the BG cells on HD. Due to loss of healthy synapses, disharmony between DP and IP increases, leading to worsen symptoms of the HD.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}