Brain inf2012(present)

Detec%ng Emo%on from EEG Signals Using the Emo%ve Epoc Device

Rafael Ramirez Zacharias Vamvakousis

Universitat Pompeu Fabra Barcelona, Spain

Presented by: Álvaro Barbosa

University of Saint Joseph Macau SAR, China

Brain Informa%cs 2012

Mo%va%on

•  Study of emo%ons in human-‐computer interac%on has increased in recent years

•  Growing need for computer applica%ons capable of detec%ng users’ emo%onal state

•  Facial and voice informa%on – can be consciously controlled and modified –  interpreta%on is oSen subjec%ve

•  Here, we use EEG-‐based emo%on detec%on

Contrib%ons •  Method for EEG-‐based emo%on detec%on

•  Use of low-‐cost technology -‐> Emo%v EPOC headset

•  We do not rely in subject self-‐reported emo%onal states (as most previous work do)

•  Instead, we use a library of emo%on-‐annotated sounds (IADS Lib -‐ h\p://csea.phhp.ufl.edu/media/iadsmessage.html)

System Overview

Data Collec%on •  6 healthy subjects (mean age = 30); listening to 12 (5-‐10s long) emo%on-‐annotated sounds (IADS Lib)

•  Emo%v EPOC headset -‐ 14 data-‐collec%ng electrodes (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8 and AF4) and 2 reference electrodes

Feature Extrac%on

•  Alpha (8-‐12Hz) and Beta (12-‐30Hz) bands are par%cular bands of interest in emo%on research for both valence and arousal

•  We apply bandpass filtering for extrac%ng alpha and beta frequency bands

•  EEG signal in four loca%ons in the prefrontal cortex: AF3, AF4, F3 and F4

•  Arousal = a(AF3+AF4+F3+F4)/b(AF3+AF4+F3+F4) •  valence = aF4 /bF4 − aF3 /bF3

Classifica%on Learning Task

•  Detect emo%onal state of mind of a person based on observed EEG data

•  We approach this problem as a two 2-‐class classifica%on problem – high/low arousal – posi%ve/nega%ve valence ArousalClassif ier ( EEGdata([ t, t +c])) → {high, low}

ValenceClassifier ( EEGdata([ t, t +c])) → {posi%ve, nega%ve}

c=1s and with increments of t of 0.0625s

Valence-‐Arousal Plane

Algorithms

•  Linear Discriminant Analysis (LDA)

•  Support Vector Machines (SVM) –  linear kernel –  radial basis func%on (RBF) kernel

•  Evalua%on: 10-‐fold cross valida%on

Results (1)

Results (2)

Results (3)

•  Results indicate that the EEG data contains sufficient info to dis%nguish between high/low arousal and posiFve/negaFve valence states

•  Machine learning methods are capable of learning the EGG pa\erns that dis%nguish these states

•  Different accuracies among different subjects •  For a subject, similar accuracies with different learning method

Results (4)

•  Inter-‐subjects accuracy differences may be due to – different degrees of emo%onal response between different individuals, or

– amount of noise for different subjects.

•  Anyway, there exists considerable varia%on in EEG responses among different subjects

Conclusion

•  Low-‐cost emo%on detec%on system •  no self-‐assessment informa%on about the emo%onal states by the subjects

•  linear discriminant analysis and support vector machines classifica%on

•  Classifiers able to discriminate between high-‐low arousal and posi%ve-‐nega%ve valence

Future work

•  Improve classifica%on accuracy – Systema%cally exploring different feature extrac%on methods and learning methods

•  Incorpora%ng self-‐assessment informa%on would very likely also improve the accuracies of the classifiers

Thank you!

Rafael <[email protected]>

Brain inf2012(present)

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