6533b82bfe1ef96bd128cef6
RESEARCH PRODUCT
Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals
Miguel Garcia-pinedaMaximo CobosMiguel Arevalillo-herráezSandra Rogersubject
Normalization (statistics)Data AnalysisSupport Vector MachineDatabases FactualComputer sciencemedia_common.quotation_subjectEmotionsData transformation (statistics)Context (language use)02 engineering and technologyvalence detectionElectroencephalographyAffect (psychology)Machine learningcomputer.software_genrelcsh:Chemical technologyBiochemistryModels BiologicalArticleAnalytical Chemistrydata transformation0202 electrical engineering electronic engineering information engineeringmedicinePersonalityHumanslcsh:TP1-1185EEGElectrical and Electronic EngineeringInstrumentationarousal detectionmedia_commonmedicine.diagnostic_testbusiness.industry020206 networking & telecommunicationsSubject (documents)ElectroencephalographySignal Processing Computer-AssistedAtomic and Molecular Physics and Opticsnormalization020201 artificial intelligence & image processingArtificial intelligencebusinessArousalcomputerdescription
Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject&rsquo
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-07-08 | Sensors |