Online recognition of facial actions for natural EEG-based BCI applications

Abstract

We present a system for classification of nine voluntary facial actions, i.e. Neutral, Smile, Sad, Surprise, Angry, Speak, Blink, Left, and Right. The data is assessed by an Emotiv EPOC wireless EEG head-set. We derive spectral features and step function features that represent the main signal characteristics of the recorded data in a straightforward manner. With a two stage classification setup using support vector machines we achieve an overall recognition accuracy of 81.8%. Furthermore, we show a qualitative evaluation of an online system for facial action recognition using the EPOC device. © 2011 Springer-Verlag.