PROJECT TITLE:

Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection

ABSTRACT:

Emotions are time varying affective phenomena that are elicited as a result of stimuli. Videos and films in particular are made to elicit emotions in their audiences. Detecting the viewers' emotions instantaneously will be used to search out the emotional traces of videos. In this paper, we tend to gift our approach in instantaneously detecting the emotions of video viewers' emotions from electroencephalogram (EEG) signals and facial expressions. A group of emotion inducing videos were shown to participants whereas their facial expressions and physiological responses were recorded. The expressed valence (negative to positive emotions) within the videos of participants' faces were annotated by 5 annotators. The stimuli videos were also continuously annotated on valence and arousal dimensions. Long-short-term-memory Recurrent Neural Networks (LSTM-RNN) and continuous conditional random fields (CCRF) were utilised in detecting emotions automatically and continuously. We tend to found the results from facial expressions to be superior to the results from EEG signals. We analyzed the impact of the contamination of facial muscle activities on EEG signals and found that most of the emotionally valuable content in EEG options are as a results of this contamination. But, our statistical analysis showed that EEG signals still carry complementary information in presence of facial expressions.