Pub Date : 2018-03-09DOI: 10.1109/IWW-BCI.2018.8311517
Yong-Jeong Kim, No-Sang Kwak, Seong-Whan Lee
Brain-computer interface (BCI) researchers have shown an increased interest in the development of ear-electroencephalography (EEG), which is a method for measuring EEG signals in the ear or around the outer ear, to provide a more convenient BCI system to users. However, the ear-EEG studies have researched mostly targeting on a visual/auditory stimuli-based BCI system or a drowsiness detection system. To the best of our knowledge, there is no study on a motor-imagery (MI) detection system based on ear-EEG. MI is one of the mostly used paradigms in BCI because it does not need any external stimuli. MI that associated with ear-EEG could facilitate useful BCI applications in real-world. Hence, in this study, we aim to investigate a feasibility of the MI classification using ear-around EEG signals. We proposed a common spatial pattern (CSP)-based frequency-band optimization algorithm and compared it with three existing methods. The best classification results for two datasets are 71.8% and 68.07%, respectively, using the ear-around EEG signals (cf. 92.40% and 91.64% using motor-area EEG signals).
{"title":"Classification of motor imagery for Ear-EEG based brain-computer interface","authors":"Yong-Jeong Kim, No-Sang Kwak, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2018.8311517","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311517","url":null,"abstract":"Brain-computer interface (BCI) researchers have shown an increased interest in the development of ear-electroencephalography (EEG), which is a method for measuring EEG signals in the ear or around the outer ear, to provide a more convenient BCI system to users. However, the ear-EEG studies have researched mostly targeting on a visual/auditory stimuli-based BCI system or a drowsiness detection system. To the best of our knowledge, there is no study on a motor-imagery (MI) detection system based on ear-EEG. MI is one of the mostly used paradigms in BCI because it does not need any external stimuli. MI that associated with ear-EEG could facilitate useful BCI applications in real-world. Hence, in this study, we aim to investigate a feasibility of the MI classification using ear-around EEG signals. We proposed a common spatial pattern (CSP)-based frequency-band optimization algorithm and compared it with three existing methods. The best classification results for two datasets are 71.8% and 68.07%, respectively, using the ear-around EEG signals (cf. 92.40% and 91.64% using motor-area EEG signals).","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"67 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2018-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83969916","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}
Pub Date : 2018-03-09DOI: 10.1109/IWW-BCI.2018.8311528
Yongkoo Park, Wonzoo Chung
In this paper, we explore locally generated cross-channel-region CSP features to improve motor imagery classification in EEG-based BCIs. We set several clustered sub-channel regions covering the entire measured channels and extract CSP features by cross-combining the sub-channel regions with each single channel. The features generated by this cross-channel-region combinations have regional information on sensor space for motor imagery and can be used to improve classification accuracy when fed to LS-SVM classifier. The performance improvement of the proposed algorithm is verified by simulations.
{"title":"Novel BCI classification method using cross-channel-region CSP features","authors":"Yongkoo Park, Wonzoo Chung","doi":"10.1109/IWW-BCI.2018.8311528","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311528","url":null,"abstract":"In this paper, we explore locally generated cross-channel-region CSP features to improve motor imagery classification in EEG-based BCIs. We set several clustered sub-channel regions covering the entire measured channels and extract CSP features by cross-combining the sub-channel regions with each single channel. The features generated by this cross-channel-region combinations have regional information on sensor space for motor imagery and can be used to improve classification accuracy when fed to LS-SVM classifier. The performance improvement of the proposed algorithm is verified by simulations.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"143 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86749399","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}
Pub Date : 2018-03-09DOI: 10.1109/IWW-BCI.2018.8311492
Yongkoo Park, Wonzoo Chung
In this paper, we present a novel motor imagery classification method in electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) using locally generated CSP features centered at each channel. By favoring the channels with the local CSP features exhibiting significant eigenvalue disparity in the classification stage, improved performance in classification accuracy can be achieved in comparison with the conventional globally optimized CSP feature, especially for small-sample setting environments. Simulation results confirm the significant performance improvement of the proposed method for BCI competition III dataset Iva using 18 channels in the motor area.
{"title":"BCI classification using locally generated CSP features","authors":"Yongkoo Park, Wonzoo Chung","doi":"10.1109/IWW-BCI.2018.8311492","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311492","url":null,"abstract":"In this paper, we present a novel motor imagery classification method in electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) using locally generated CSP features centered at each channel. By favoring the channels with the local CSP features exhibiting significant eigenvalue disparity in the classification stage, improved performance in classification accuracy can be achieved in comparison with the conventional globally optimized CSP feature, especially for small-sample setting environments. Simulation results confirm the significant performance improvement of the proposed method for BCI competition III dataset Iva using 18 channels in the motor area.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"274 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77190406","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}
Pub Date : 2018-01-16DOI: 10.1109/IWW-BCI.2018.8311533
Eojin Rho, Sungho Jo
In this paper, we propose a 3-D model based semi-autonomous navigation system with biosignal classification to control a quadcopter. Recently, some studies have proposed semi-autonomous navigation systems to resolve the inaccuracy of biosignal classification. However, these studies are based on 2-D models, which are inappropriate for 3-D real environments. This semi-autonomous navigation system resolves the limitations of the aforementioned papers by modeling the environment with an efficient 3-D model called OctoMap and uses this model to find a path that avoids obstacles. The performance of this proposed system was evaluated by comparing our system with the 2-D model based system mentioned above. This result shows the feasibility of our semi-autonomous system with OctoMap to control the quadcopter in 3-D space.
{"title":"OctoMap-based semi-autonomous quadcopter navigation with biosignal classification","authors":"Eojin Rho, Sungho Jo","doi":"10.1109/IWW-BCI.2018.8311533","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311533","url":null,"abstract":"In this paper, we propose a 3-D model based semi-autonomous navigation system with biosignal classification to control a quadcopter. Recently, some studies have proposed semi-autonomous navigation systems to resolve the inaccuracy of biosignal classification. However, these studies are based on 2-D models, which are inappropriate for 3-D real environments. This semi-autonomous navigation system resolves the limitations of the aforementioned papers by modeling the environment with an efficient 3-D model called OctoMap and uses this model to find a path that avoids obstacles. The performance of this proposed system was evaluated by comparing our system with the 2-D model based system mentioned above. This result shows the feasibility of our semi-autonomous system with OctoMap to control the quadcopter in 3-D space.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"5 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83855126","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}
Pub Date : 2018-01-16DOI: 10.1109/IWW-BCI.2018.8311519
Netiwit Kaongoen, Sungho Jo
Ear-EEG is an EEG acquisition method that record EEG signal from inside the user's ear. This study fabricated an ear-EEG device and tested its ability to detect alpha activity when the subject is in a wakeful relaxation state and its performance in binary auditory P300 BCI system. The ear-EEG fabricated in this study were able to detect the alpha activity from the subjects. In auditory P300 experiment, the highest accuracy and ITR was 95.61% and 2.9685 bits/min, respectively. The surveys given to the participants point out that the ear-EEG devices in this work were easily wearable and very comfortably. These results suggest that ear-EEG is a promising alternative EEG-acquisition method that is more user-friendly and suitable for BCI system that aims for daily-life usage comparing to the conventional scalp-EEG method.
{"title":"An auditory P300-based brain-computer interface using Ear-EEG","authors":"Netiwit Kaongoen, Sungho Jo","doi":"10.1109/IWW-BCI.2018.8311519","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311519","url":null,"abstract":"Ear-EEG is an EEG acquisition method that record EEG signal from inside the user's ear. This study fabricated an ear-EEG device and tested its ability to detect alpha activity when the subject is in a wakeful relaxation state and its performance in binary auditory P300 BCI system. The ear-EEG fabricated in this study were able to detect the alpha activity from the subjects. In auditory P300 experiment, the highest accuracy and ITR was 95.61% and 2.9685 bits/min, respectively. The surveys given to the participants point out that the ear-EEG devices in this work were easily wearable and very comfortably. These results suggest that ear-EEG is a promising alternative EEG-acquisition method that is more user-friendly and suitable for BCI system that aims for daily-life usage comparing to the conventional scalp-EEG method.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"5 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88611186","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}
Pub Date : 2018-01-16DOI: 10.1109/IWW-BCI.2018.8311524
Sungho Jo, Jin Woo Choi
In this study, we propose an effective training method for 2-class motor imagery tasks on brain computer interface (BCI) systems viable even for distracting environments. For non-invasive BCIs, it is difficult to capture event-related desynchronization (ERD) and event-related synchronization (ERS) signals through electroencephalogram (EEG) in places where it is difficult for subjects to concentrate. To improve concentration under a distracting environment, our proposed training method implemented a graphical interface as a source of visual feedback. The performance of the implemented training method is evaluated by comparing its results with those of a training method that does not support visual feedback. The experiments are held while a variety of noises are produced to simulate a distracting environment. The results of the experiment demonstrate the effectiveness of the proposed training method in distracting environments for 2-class motor imagery tasks.
{"title":"Effective motor imagery training with visual feedback for non-invasive brain computer interface","authors":"Sungho Jo, Jin Woo Choi","doi":"10.1109/IWW-BCI.2018.8311524","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311524","url":null,"abstract":"In this study, we propose an effective training method for 2-class motor imagery tasks on brain computer interface (BCI) systems viable even for distracting environments. For non-invasive BCIs, it is difficult to capture event-related desynchronization (ERD) and event-related synchronization (ERS) signals through electroencephalogram (EEG) in places where it is difficult for subjects to concentrate. To improve concentration under a distracting environment, our proposed training method implemented a graphical interface as a source of visual feedback. The performance of the implemented training method is evaluated by comparing its results with those of a training method that does not support visual feedback. The experiments are held while a variety of noises are produced to simulate a distracting environment. The results of the experiment demonstrate the effectiveness of the proposed training method in distracting environments for 2-class motor imagery tasks.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"26 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78158712","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}
Pub Date : 2018-01-15DOI: 10.1109/IWW-BCI.2018.8311498
G. Müller-Putz, J. Pereira, P. Ofner, A. Schwarz, C. Dias, Reinmar J. Kobler, Lea Hehenberger, A. Pinegger, A. Sburlea
Spinal cord injury (SCI) can disrupt the communication pathways between the brain and the rest of the body, restricting the ability to perform volitional movements. Neuroprostheses or robotic arms can enable individuals with SCI to move independently, improving their quality of life. The control of restorative or assistive devices is facilitated by brain-computer interfaces (BCIs), which convert brain activity into control commands. In this paper, we summarize the recent findings of our research towards the main aim to provide reliable and intuitive control. We propose a framework that encompasses the detection of goal-directed movement intention, movement classification and decoding, error-related potentials detection and delivery of kinesthetic feedback. Finally, we discuss future directions that could be promising to translate the proposed framework to individuals with SCI.
{"title":"Towards non-invasive brain-computer interface for hand/arm control in users with spinal cord injury","authors":"G. Müller-Putz, J. Pereira, P. Ofner, A. Schwarz, C. Dias, Reinmar J. Kobler, Lea Hehenberger, A. Pinegger, A. Sburlea","doi":"10.1109/IWW-BCI.2018.8311498","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311498","url":null,"abstract":"Spinal cord injury (SCI) can disrupt the communication pathways between the brain and the rest of the body, restricting the ability to perform volitional movements. Neuroprostheses or robotic arms can enable individuals with SCI to move independently, improving their quality of life. The control of restorative or assistive devices is facilitated by brain-computer interfaces (BCIs), which convert brain activity into control commands. In this paper, we summarize the recent findings of our research towards the main aim to provide reliable and intuitive control. We propose a framework that encompasses the detection of goal-directed movement intention, movement classification and decoding, error-related potentials detection and delivery of kinesthetic feedback. Finally, we discuss future directions that could be promising to translate the proposed framework to individuals with SCI.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75403945","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}
Pub Date : 2018-01-15DOI: 10.1109/IWW-BCI.2018.8311508
Batyrkhan Saduanov, Tohid Alizadeh, J. An, B. Abibullaev
Onerous life of paralyzed people is a substantial problem of the world society and improving their life quality would be a great achievement. This paper proposes a solution in this regard based on telepresence, where a patient perceives and interacts with a world through an embodiment of a robot controlled by a Brain-Computer Interface (BCI) system. The proposed approach brings together two leading techniques: Programming by Demonstration and BCI. Several tasks could be learned by the robot observing someone performing the function. The end user would issue commands to the robot, using a BCI system, concerning its movement and the tasks to be performed. An experiment is designed and conducted, verifying the applicability of the proposed approach.
{"title":"Trained by demonstration humanoid robot controlled via a BCI system for telepresence","authors":"Batyrkhan Saduanov, Tohid Alizadeh, J. An, B. Abibullaev","doi":"10.1109/IWW-BCI.2018.8311508","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311508","url":null,"abstract":"Onerous life of paralyzed people is a substantial problem of the world society and improving their life quality would be a great achievement. This paper proposes a solution in this regard based on telepresence, where a patient perceives and interacts with a world through an embodiment of a robot controlled by a Brain-Computer Interface (BCI) system. The proposed approach brings together two leading techniques: Programming by Demonstration and BCI. Several tasks could be learned by the robot observing someone performing the function. The end user would issue commands to the robot, using a BCI system, concerning its movement and the tasks to be performed. An experiment is designed and conducted, verifying the applicability of the proposed approach.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81494188","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}
Pub Date : 2018-01-15DOI: 10.1109/IWW-BCI.2018.8311513
Gihyoun Lee, S. Jin, Seong Tae Yang, J. An, B. Abibullaev
The general linear model (GLM) as a standard model for fMRI analysis has been applied to fNIRS imaging analysis as well. The GLM is very likely to make false predictions for motion artifact in fNIRS signals. The temporal characteristics of normal cerebral hemodynamics are basically the opposite tendency of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR). When motion artifact occurs, HbO and HbR are completely different from normal cases as the baseline changes. This paper presents a cross-correlation between HbO and HbR as a feature that can determine the dynamic noise of fNIRS. Since cross-correlation is a deterministic tool that is easy to calculate, it will be very useful for noise elimination if it is noted as a criterion of dynamic noise in fNIRS signals.
{"title":"Cross-correlation between HbO and HbR as an effective feature of motion artifact in fNIRS signal","authors":"Gihyoun Lee, S. Jin, Seong Tae Yang, J. An, B. Abibullaev","doi":"10.1109/IWW-BCI.2018.8311513","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311513","url":null,"abstract":"The general linear model (GLM) as a standard model for fMRI analysis has been applied to fNIRS imaging analysis as well. The GLM is very likely to make false predictions for motion artifact in fNIRS signals. The temporal characteristics of normal cerebral hemodynamics are basically the opposite tendency of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR). When motion artifact occurs, HbO and HbR are completely different from normal cases as the baseline changes. This paper presents a cross-correlation between HbO and HbR as a feature that can determine the dynamic noise of fNIRS. Since cross-correlation is a deterministic tool that is easy to calculate, it will be very useful for noise elimination if it is noted as a criterion of dynamic noise in fNIRS signals.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"23 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83265241","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}
Pub Date : 2018-01-05DOI: 10.1109/IWW-BCI.2018.8311527
Artemiy Oleinikov, B. Abibullaev, A. Shintemirov, M. Folgheraiter
Electromyography (EMG) signal analysis is one of the key determinants of the effectiveness of prosthetic devices. Modern researchers provide various methods of detection of different hand movements and postures. In this work, we examined the possibility to produce efficient detection of hand movement to a specific posture with the minimum possible number of electrodes. The data acquisition is produced with 1 channel BiTalino EMG sensor based on bipolar differential measurement. Using feature extraction and artificial neural network we achieved 82% of offline classification accuracy for 8 hand motions and 91% accuracy for 6 hand motions based on 200 ms of EMG signal. Also, the motion detection algorithm was developed and successfully tested that allowed to implement the algorithm for real-time classification and that showed sufficient accuracy for 2 and 4 motion classes cases.
{"title":"Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks","authors":"Artemiy Oleinikov, B. Abibullaev, A. Shintemirov, M. Folgheraiter","doi":"10.1109/IWW-BCI.2018.8311527","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311527","url":null,"abstract":"Electromyography (EMG) signal analysis is one of the key determinants of the effectiveness of prosthetic devices. Modern researchers provide various methods of detection of different hand movements and postures. In this work, we examined the possibility to produce efficient detection of hand movement to a specific posture with the minimum possible number of electrodes. The data acquisition is produced with 1 channel BiTalino EMG sensor based on bipolar differential measurement. Using feature extraction and artificial neural network we achieved 82% of offline classification accuracy for 8 hand motions and 91% accuracy for 6 hand motions based on 200 ms of EMG signal. Also, the motion detection algorithm was developed and successfully tested that allowed to implement the algorithm for real-time classification and that showed sufficient accuracy for 2 and 4 motion classes cases.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"229 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80225448","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}
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