Affective, natural interaction using EEG: Sensors, application and future directions

Hondrou, C; Caridakis, G

dc.contributor.author	Hondrou, C	en
dc.contributor.author	Caridakis, G	en
dc.date.accessioned	2014-03-01T02:53:32Z
dc.date.available	2014-03-01T02:53:32Z
dc.date.issued	2012	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36404
dc.subject	Affect aware applications	en
dc.subject	Affective Computing	en
dc.subject	EEG	en
dc.subject	Natural Interaction	en
dc.subject.other	Affective Computing	en
dc.subject.other	Application domains	en
dc.subject.other	Dimensionality reduction	en
dc.subject.other	EEG signals	en
dc.subject.other	Emotion representation	en
dc.subject.other	Induction method	en
dc.subject.other	Multiple modalities	en
dc.subject.other	Natural interactions	en
dc.subject.other	Network communications	en
dc.subject.other	Physiological signals	en
dc.subject.other	Technological advancement	en
dc.subject.other	Technological feature	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Electrophysiology	en
dc.subject.other	Sensors	en
dc.subject.other	Electroencephalography	en
dc.title	Affective, natural interaction using EEG: Sensors, application and future directions	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-30448-4_42	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-30448-4_42	en
heal.publicationDate	2012	en
heal.abstract	ElectroEncephaloGraphy signals have been studied in relation to emotion even prior to the establishment of Affective Computing as a research area. Technological advancements in the sensor and network communication technology allowed EEG collection during interaction with low obtrusiveness levels as opposed to earlier work which classified physiological signals as the most obtrusive modality in affective analysis. The current article provides a critical survey of research work dealing with broadly affective analysis of EEG signals collected during natural or naturalistic interaction. It focuses on sensors that allow such natural interaction (namely NeuroSky and Emotiv), related technological features and affective aspects of applications in several application domains. These aspects include emotion representation approach, induction method and stimuli and annotation chosen for the application. Additionally, machine learning issues related to affective analysis (such as incorporation of multiple modalities and related issues, feature selection for dimensionality reduction and classification architectures) are revised. Finally, future directions of EEG incorporation in affective and natural interaction are discussed. © 2012 Springer-Verlag.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-30448-4_42	en
dc.identifier.volume	7297 LNCS	en
dc.identifier.spage	331	en
dc.identifier.epage	338	en