2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)最新文献_第5页

Facial Emotion Recognition Based on Brain and Machine Collaborative Intelligence 基于脑机协同智能的面部情绪识别

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071606

Wenfen Ling, Wanzeng Kong, Yanfang Long, Can Yang, Xuanyu Jin

{"title":"Facial Emotion Recognition Based on Brain and Machine Collaborative Intelligence","authors":"Wenfen Ling, Wanzeng Kong, Yanfang Long, Can Yang, Xuanyu Jin","doi":"10.1109/CIVEMSA45640.2019.9071606","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071606","url":null,"abstract":"Facial emotion is an important way for humans to convey the feeling and feed back to others. It is also a key component of human-computer interaction systems(HCISs). Naturally, facial emotion recognition(FER) has become a hot topic of current research. At present, the methods of FER typically rely on vision, using computer technology to extract visual features from face images. However, these features are derived from data-driven models, lacking the cognitive minds from the brain, so the recognition performance is not ideal in some cases. Factually, EEG features evoked by facial emotion images have high-level representations of emotion and good discrimination. For this, we propose a novel brain-machine collaborative method for FER. Firstly, EEG emotional features are extracted from the EEG signals collected when people observe emotion images. Secondly, the image visual features are extracted from the original facial emotion images. Thirdly, a regression model is used to find a mapping relationship between these two features in training stage. Finally, the EEG-like features predicted by pre-trained regression model are used in the test set to identify emotions. This method has been verified on CFAPS and found that the average recognition accuracy of the seven emotions is 88.28%, which is better than the simple image-based method.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134148482","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}

引用次数: 1

Motor imagery signal classification based on transfer learning 基于迁移学习的运动意象信号分类

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071624

Banghua Yang, Minmin Zheng, Cuntai Guan, Li Bo

{"title":"Motor imagery signal classification based on transfer learning","authors":"Banghua Yang, Minmin Zheng, Cuntai Guan, Li Bo","doi":"10.1109/CIVEMSA45640.2019.9071624","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071624","url":null,"abstract":"The EEG of motor imagery varies greatly according to different subjects and the same subject in different time periods. Traditional machine learning methods can only solve the classification and recognition of the same individual within a short period of time, and the classification and recognition effect also depends on the difference of data sets, with strong individual differences. Many classification methods are unstable and have poor universality. Transfer learning can use knowledge from similar data to enhance the learning process, and use knowledge in related fields to help complete the learning tasks in the target field, so as to change the traditional learning from scratch into accumulated learning and improve learning efficiency. In this paper, the power spectrum characteristics of 8 channels signals related to motor imagery at 7-29hz were extracted, and the motor imagery data were classified and modeled by transfer learning algorithm. Meanwhile, compared with the other two existing classification methods PSD (Power Spectral Density) and CSP (Common Spatial Pattern), the analysis results showed that the classification accuracy of transfer learning (90.9 ± 2.2) was higher than that of traditional PSD+LDA(62.5±11.6) and CSP+SVM (71.3±3.5), which verified the feasibility of transfer learning in motor imagery BCI classification and recognition.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132617104","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}

引用次数: 1

A comparison of classification methods for recognizing single-trial ERP in RSVP-based brain-computer interfaces 基于rsvp的脑机接口识别单试验ERP的分类方法比较

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071625

Xiaolin Xiao, Minpeng Xu, Dong Ming

{"title":"A comparison of classification methods for recognizing single-trial ERP in RSVP-based brain-computer interfaces","authors":"Xiaolin Xiao, Minpeng Xu, Dong Ming","doi":"10.1109/CIVEMSA45640.2019.9071625","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071625","url":null,"abstract":"Event-related potentials (ERPs) are one of the most popular control signals for brain-computer interfaces (BCIs). Fast classifying ERPs is vital for the good performance of ERP BCIs. However, due to noisy background electroencephalography (EEG) environments, current ERP-based BCI systems need to collect multiple trials for a reliable output, which is inefficient. This study compared a recently developed algorithm, i.e. discriminative canonical pattern matching (DCPM), with five traditional classification methods, i.e. linear discriminant analysis (LDA), four advanced methods of LDA included stepwise LDA, Bayesian LDA, shrinkage LDA and spatial-temporal discriminant analysis (STDA), for the detection of single-trial ERPs with a small number of training samples. Public dataset from RSVP-speller, which would induce ERPs contained N200 and P300 components in ERPs, was addressed in this study. Study results showed that the DCPM significantly outperformed the other traditional methods in single-trial ERP classification in RSVP-based BCI even with small training samples, suggesting the DCPM is a promising classification algorithm for the ERP-BCI.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127968511","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}

引用次数: 0

Improved RCSP and AdaBoost-based classification for Motor-Imagery BCI 基于RCSP和adaboost的运动图像脑机接口改进分类

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071599

Yangyang Miao, Feiyu Yin, Cili Zuo, Xingyu Wang, Jing Jin

引用次数: 7

Phased Transducer and Drive System Design 相控换能器与驱动系统设计

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071590

Hao Zhang, Yanqiu Zhang, X. Jian

引用次数: 3

Research on Power-Assisted Strategy and Device Based on Muscle Synergy 基于肌肉协同的动力辅助策略及装置研究

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071628

Lintao Hu, Xiuying Luo, Shangjie Tang, Xiaoying Wu, Lin Chen, Xiaolin Zheng, W. Hou

{"title":"Research on Power-Assisted Strategy and Device Based on Muscle Synergy","authors":"Lintao Hu, Xiuying Luo, Shangjie Tang, Xiaoying Wu, Lin Chen, Xiaolin Zheng, W. Hou","doi":"10.1109/CIVEMSA45640.2019.9071628","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071628","url":null,"abstract":"Surface EMG (sEMG) signals are non-invasive means of recording muscle activity that reflect the activation of human skeletal muscles. The purpose of this research is to study a dynamic assisted strategy based on muscle synergy, and to design an upper limb motion assisted device to achieve different assist tasks. Eleven healthy participants were recruited for the study. The participants were asked to perform grasping tasks at 9 target locations in the space and sEMG signals of the eight involved muscles were recorded. The non-negative matrix factorization (NMF) algorithm was applied to extract muscle coordination information during each corresponding experimental task. According to the muscle coordination information of all the participants' sEMG signals extracted by NMF decomposition, the corresponding upper limb motion tasks were decoded. The proposed method can decode the movement pattern of the human arm by considering the mapping relationship between the muscle coordination information and the joint motion, which may provide less effortful control of the robotic exoskeleton for rehabilitation training of individuals with neurological disorders or arm impairment.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115146621","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}

引用次数: 1