Towards emotion recognition for virtual environments [Elektronisk resurs] an evaluation of eeg features on benchmark dataset

• One of the challenges in virtual environments 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 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 userâs emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russellâs Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the and waves and High Order Crossing of the EEG signal. © 2017, The Author(s). 

Ämnesord

Engineering and Technology  (hsv)

Electrical Engineering, Electronic Engineering, Information Engineering  (hsv)

Signal Processing  (hsv)

Teknik och teknologier  (hsv)

Elektroteknik och elektronik  (hsv)

Signalbehandling  (hsv)

Genre

government publication  (marcgt)

Indexterm och SAB-rubrik

Classification (of information)

Decision trees

Electroencephalography

Feature extraction

Speech recognition

Virtual reality

Affective Computing

Affective state

Benchmark datasets

Circumplex models

Classification accuracy

Electroencephalogram signals

Emotion recognition

Interaction experiences

Behavioral research