C4.5: programs for machine learning
C4.5: programs for machine learning
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
User Modeling and User-Adapted Interaction
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
COMPSAC '00 24th International Computer Software and Applications Conference
Learning ontologies from natural language texts
International Journal of Human-Computer Studies
Analysis of emotion recognition using facial expressions, speech and multimodal information
Proceedings of the 6th international conference on Multimodal interfaces
International Journal of Human-Computer Studies
Development of NeuroElectroMagnetic ontologies(NEMO): a framework for mining brainwave ontologies
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Ontology Based Affective Context Representation
EATIS '07 Proceedings of the 2007 Euro American conference on Telematics and information systems
Towards an Ontology for Describing Emotions
WSKS '08 Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society
Towards a RESTful Plug and Play Experience in the Web of Things
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Building the Internet of Things Using RFID: The RFID Ecosystem Experience
IEEE Internet Computing
Web of Things as a Framework for Ubiquitous Intelligence and Computing
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
UAHCI '09 Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Addressing Diversity. Part I: Held as Part of HCI International 2009
Ontological reasoning to configure emotional voice synthesis
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
Creating Kansei engineering-based ontology for annotating and archiving photos database
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction design and usability
Web intelligence meets brain informatics
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Annotation of emotions and feelings in texts
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
IEEE Transactions on Affective Computing
Research challenges and perspectives on Wisdom Web of Things (W2T)
The Journal of Supercomputing
Ontology driven decision support for the diagnosis of mild cognitive impairment
Computer Methods and Programs in Biomedicine
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We describe an ontological model for representation and integration of electroencephalographic (EEG) data and apply it to detect human emotional states. The model (BIO_EMOTION) is an ontology-based context model for emotion recognition and acts as a basis for: (1) the modeling of users' contexts, including user profiles, EEG data, the situation and environment factors, and (2) supporting reasoning on the users' emotional states. Because certain ontological concepts in the EEG domain are ill-defined, we formally represent and store these concepts, their taxonomies and high-level representation (i.e., rules) in the model. To evaluate the effectiveness for inferring emotional states, DEAP dataset is used for model reasoning. Result shows that our model reaches an average recognition ratio of 75.19 % on Valence and 81.74 % on Arousal for eight participants. As mentioned above, the BIO-EMOTION model acts like a bridge between users' emotional states and low-level bio-signal features. It can be integrated in user modeling techniques, and be used to model web users' emotional states in human-centric web aiming to provide active, transparent, safe and reliable services to users. This work aims at, in other words, creating an ontology-based context model for emotion recognition using EEG. Particularly, this model completely implements the loop body of the W2T data cycle once: from low-level EEG feature acquisition to emotion recognition. A long-term goal for the study is to complete this model to implement the whole W2T data cycle.