Scientific Presentations - Abstracts list

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. In spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing. Title Short-term emotion assessment in a recall paradigm. Abstract The work presented in this paper aims at assessing human emotions using peripheral as well as electroencephalographic (EEG) physiological signals on short-time periods. Three specific areas of the valence-arousal emotional space are defined, corresponding to negatively excited, positively excited, and calm-neutral states. An acquisition protocol based on the recall of past emotional events has been designed to acquire data from both peripheral and EEG signals. Pattern classification is used to distinguish between the three areas of the valence- arousal space. The performance of several classifiers has been evaluated on ten participants and different feature sets: peripheral features, EEG time-frequency features, EEG pairwise mutual information features. Comparison of results obtained using either peripheral or EEG signals confirms the interest of using EEG's to assess valence and arousal in emotion recall conditions. The obtained accuracy for the three emotional classes are of 63% using EEG time- frequency features which is better than the results obtained from previous studies using EEG and similar classes. Fusion of the different feature sets at the decision level using a summation rule also showed to improve accuracy to 70%. Furthermore, the rejection of non confident samples finally led to a classification accuracy of 80% for the three classes.