{"title":"基于局部子集特征选择和改进的Dempster-Shafer理论的情绪识别新方法。","authors":"Morteza Zangeneh Soroush, Keivan Maghooli, Seyed Kamaledin Setarehdan, Ali Motie Nasrabadi","doi":"10.1186/s12993-018-0149-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Emotion recognition is an increasingly important field of research in brain computer interactions.</p><p><strong>Introduction: </strong>With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature.</p><p><strong>Methods: </strong>The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence.</p><p><strong>Results: </strong>The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced.</p><p><strong>Conclusions: </strong>The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.</p>","PeriodicalId":8729,"journal":{"name":"Behavioral and Brain Functions","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12993-018-0149-4","citationCount":"29","resultStr":"{\"title\":\"A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.\",\"authors\":\"Morteza Zangeneh Soroush, Keivan Maghooli, Seyed Kamaledin Setarehdan, Ali Motie Nasrabadi\",\"doi\":\"10.1186/s12993-018-0149-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Emotion recognition is an increasingly important field of research in brain computer interactions.</p><p><strong>Introduction: </strong>With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature.</p><p><strong>Methods: </strong>The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence.</p><p><strong>Results: </strong>The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced.</p><p><strong>Conclusions: </strong>The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.</p>\",\"PeriodicalId\":8729,\"journal\":{\"name\":\"Behavioral and Brain Functions\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2018-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1186/s12993-018-0149-4\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavioral and Brain Functions\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1186/s12993-018-0149-4\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BEHAVIORAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavioral and Brain Functions","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1186/s12993-018-0149-4","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.
Background: Emotion recognition is an increasingly important field of research in brain computer interactions.
Introduction: With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature.
Methods: The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence.
Results: The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced.
Conclusions: The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.
期刊介绍:
A well-established journal in the field of behavioral and cognitive neuroscience, Behavioral and Brain Functions welcomes manuscripts which provide insight into the neurobiological mechanisms underlying behavior and brain function, or dysfunction. The journal gives priority to manuscripts that combine both neurobiology and behavior in a non-clinical manner.