{"title":"基于多生理信号的情绪识别系统设计","authors":"Huiping Jiang, Guosheng Yang, X. Gui, Naiyu Wu, Ting Zhang","doi":"10.1109/ICCI-CC.2012.6311199","DOIUrl":null,"url":null,"abstract":"A physiological signal-based emotion recognition system was designed. The system was developed to operate as a use r-independent system, based on three physiological signals databases obtained from multiple ethnic objections. The input signals were EEG, eye activity and facial expressions, all of which were acquired synchronous. The whole system will be comfort from the subjects' body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted.","PeriodicalId":427778,"journal":{"name":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Emotion recognition system design using multi-physiological signals\",\"authors\":\"Huiping Jiang, Guosheng Yang, X. Gui, Naiyu Wu, Ting Zhang\",\"doi\":\"10.1109/ICCI-CC.2012.6311199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A physiological signal-based emotion recognition system was designed. The system was developed to operate as a use r-independent system, based on three physiological signals databases obtained from multiple ethnic objections. The input signals were EEG, eye activity and facial expressions, all of which were acquired synchronous. The whole system will be comfort from the subjects' body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted.\",\"PeriodicalId\":427778,\"journal\":{\"name\":\"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCI-CC.2012.6311199\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCI-CC.2012.6311199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Emotion recognition system design using multi-physiological signals
A physiological signal-based emotion recognition system was designed. The system was developed to operate as a use r-independent system, based on three physiological signals databases obtained from multiple ethnic objections. The input signals were EEG, eye activity and facial expressions, all of which were acquired synchronous. The whole system will be comfort from the subjects' body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted.