{"title":"EEG Hyperscanning for Eight or more Persons - Feasibility Study for Emotion Recognition using Deep Learning Technique","authors":"Sunghan Lee, Sangjun Han, S. Jun","doi":"10.23919/APSIPA.2018.8659738","DOIUrl":null,"url":null,"abstract":"Multi-user electroencephalogram (EEG) system is necessary to study concurrent activity among many persons. It is difficult to find a system that measures multiple EEG signals from more than even three people simultaneously. Therefore, we suggested a framework that is able to acquire EEG signals of more than eight persons at the same time and investigated the feasibility of this system. Acquisition was performed by using OpenViBE software developed by INRIA. Wireless EEG devices for our proposed framework were manufactured by BioBrain, Corp. in Korea. A device consists of eight channels measuring frontal EEG at a speed of 1 KHz sampling rate. While participants wore this system and did emotional video watching task as a group audience, their brain signals were acquired. To show its feasibility and efficacy, our preliminary result is analyzed using deep learning technique.","PeriodicalId":287799,"journal":{"name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/APSIPA.2018.8659738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Multi-user electroencephalogram (EEG) system is necessary to study concurrent activity among many persons. It is difficult to find a system that measures multiple EEG signals from more than even three people simultaneously. Therefore, we suggested a framework that is able to acquire EEG signals of more than eight persons at the same time and investigated the feasibility of this system. Acquisition was performed by using OpenViBE software developed by INRIA. Wireless EEG devices for our proposed framework were manufactured by BioBrain, Corp. in Korea. A device consists of eight channels measuring frontal EEG at a speed of 1 KHz sampling rate. While participants wore this system and did emotional video watching task as a group audience, their brain signals were acquired. To show its feasibility and efficacy, our preliminary result is analyzed using deep learning technique.