Publications:Feature Extraction for Emotion Recognition and Modelling using Neurophysiological Data
From ISLAB/CAISR
| Title | Feature Extraction for Emotion Recognition and Modelling using Neurophysiological Data |
|---|---|
| Author | Anas Samara and Maria Luiza Recena Menezes and Leo Galway |
| Year | 2016 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Proceedings - 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 8th International Symposium on Cyberspace and Security, IUCC-CSS 2016 |
| Conference | 15th International Conference on Ubiquitous Computing and Communications (IUCC) / 8th International Symposium on Cyberspace and Security (CSS), DEC 14-16, 2016, Granada, Spain |
| DOI | http://dx.doi.org/10.1109/IUCC-CSS.2016.027 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1163434 |
| Abstract | The ubiquitous computing paradigm is becoming a reality; we are reaching a level of automation and computing in which people and devices interact seamlessly. However, one of the main challenges is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users' emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram (EEG) as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the users emotions. In this context, this paper investigates feature vector generation from EEG signals for the purpose of affective state modelling based on Russells Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect and interaction experiences through exploitation of different input modalities. The DEAP dataset was used within this work, along with a Support Vector Machine, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements, band power from the α, β, δ and θ waves, and High Order Crossing of the EEG signal. © 2016 IEEE. |
