M. Soleymani, Frank Villaro-Dixon, T. Pun, G. Chanel
{"title":"Toolbox for Emotional feAture extraction from Physiological signals (TEAP)","authors":"M. Soleymani, Frank Villaro-Dixon, T. Pun, G. Chanel","doi":"10.3389/fict.2017.00001","DOIUrl":null,"url":null,"abstract":"Physiological response is an important component of an emotional episode. In this paper, we introduce a Toolbox for Emotional feAture Extraction from Physiological signals (TEAP). This open source toolbox can preprocess and calculate emotionally relevant features from multiple physiological signals, namely, electroencephalogram (EEG), galvanic skin response (GSR), electromyogram (EMG), skin temperature, respiration pattern and blood volume pulse. The features from this toolbox are tested on two publicly available databases, i.e., MAHNOB-HCI and DEAP. We demonstrated that we achieve similar performance to the original work with the features from this toolbox. The toolbox is implemented in MATLAB and is also compatible with Octave. We hope this toolbox to be further developed and accelerate research in affective physiological signal analysis.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"10 1","pages":"1"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"59","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in ICT","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fict.2017.00001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 59
Abstract
Physiological response is an important component of an emotional episode. In this paper, we introduce a Toolbox for Emotional feAture Extraction from Physiological signals (TEAP). This open source toolbox can preprocess and calculate emotionally relevant features from multiple physiological signals, namely, electroencephalogram (EEG), galvanic skin response (GSR), electromyogram (EMG), skin temperature, respiration pattern and blood volume pulse. The features from this toolbox are tested on two publicly available databases, i.e., MAHNOB-HCI and DEAP. We demonstrated that we achieve similar performance to the original work with the features from this toolbox. The toolbox is implemented in MATLAB and is also compatible with Octave. We hope this toolbox to be further developed and accelerate research in affective physiological signal analysis.