{"title":"基于反馈人工洗牌牧羊人优化的深度Maxout网络用于脑电信号的人类情绪识别","authors":"K. S. Bhanumathi, D. Jayadevappa, Satish Tunga","doi":"10.1155/2022/3749413","DOIUrl":null,"url":null,"abstract":"Emotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is designed using the proposed feedback artificial shuffled shepherd optimization- (FASSO-) based deep maxout network (DMN) for recognizing emotions using EEG signals. The proposed technique incorporates feedback artificial tree (FAT) algorithm and shuffled shepherd optimization algorithm (SSOA). Here, median filter is used for preprocessing to remove the noise present in the EEG signals. The features, like DWT, spectral flatness, logarithmic band power, fluctuation index, spectral decrease, spectral roll-off, and relative energy, are extracted to perform further processing. Based on the data augmented results, emotion recognition can be accomplished using the DMN, where the training process of the DMN is performed using the proposed FASSO method. Furthermore, the experimental results and performance analysis of the proposed algorithm provide efficient performance with respect to accuracy, specificity, and sensitivity with the maximal values of 0.889, 0.89, and 0.886, respectively.","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals\",\"authors\":\"K. S. Bhanumathi, D. Jayadevappa, Satish Tunga\",\"doi\":\"10.1155/2022/3749413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Emotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is designed using the proposed feedback artificial shuffled shepherd optimization- (FASSO-) based deep maxout network (DMN) for recognizing emotions using EEG signals. The proposed technique incorporates feedback artificial tree (FAT) algorithm and shuffled shepherd optimization algorithm (SSOA). Here, median filter is used for preprocessing to remove the noise present in the EEG signals. The features, like DWT, spectral flatness, logarithmic band power, fluctuation index, spectral decrease, spectral roll-off, and relative energy, are extracted to perform further processing. Based on the data augmented results, emotion recognition can be accomplished using the DMN, where the training process of the DMN is performed using the proposed FASSO method. Furthermore, the experimental results and performance analysis of the proposed algorithm provide efficient performance with respect to accuracy, specificity, and sensitivity with the maximal values of 0.889, 0.89, and 0.886, respectively.\",\"PeriodicalId\":45630,\"journal\":{\"name\":\"International Journal of Telemedicine and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2022-01-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Telemedicine and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2022/3749413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Telemedicine and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2022/3749413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals
Emotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is designed using the proposed feedback artificial shuffled shepherd optimization- (FASSO-) based deep maxout network (DMN) for recognizing emotions using EEG signals. The proposed technique incorporates feedback artificial tree (FAT) algorithm and shuffled shepherd optimization algorithm (SSOA). Here, median filter is used for preprocessing to remove the noise present in the EEG signals. The features, like DWT, spectral flatness, logarithmic band power, fluctuation index, spectral decrease, spectral roll-off, and relative energy, are extracted to perform further processing. Based on the data augmented results, emotion recognition can be accomplished using the DMN, where the training process of the DMN is performed using the proposed FASSO method. Furthermore, the experimental results and performance analysis of the proposed algorithm provide efficient performance with respect to accuracy, specificity, and sensitivity with the maximal values of 0.889, 0.89, and 0.886, respectively.
期刊介绍:
The overall aim of the International Journal of Telemedicine and Applications is to bring together science and applications of medical practice and medical care at a distance as well as their supporting technologies such as, computing, communications, and networking technologies with emphasis on telemedicine techniques and telemedicine applications. It is directed at practicing engineers, academic researchers, as well as doctors, nurses, etc. Telemedicine is an information technology that enables doctors to perform medical consultations, diagnoses, and treatments, as well as medical education, away from patients. For example, doctors can remotely examine patients via remote viewing monitors and sound devices, and/or sampling physiological data using telecommunication. Telemedicine technology is applied to areas of emergency healthcare, videoconsulting, telecardiology, telepathology, teledermatology, teleophthalmology, teleoncology, telepsychiatry, teledentistry, etc. International Journal of Telemedicine and Applications will highlight the continued growth and new challenges in telemedicine, applications, and their supporting technologies, for both application development and basic research. Papers should emphasize original results or case studies relating to the theory and/or applications of telemedicine. Tutorial papers, especially those emphasizing multidisciplinary views of telemedicine, are also welcome. International Journal of Telemedicine and Applications employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer-review process.