Andreas Scheck

Repeatability of facial electromyography (EMG) activity over corrugator supercilii and zygomaticus major on differentiating various emotions

Recent affective computing findings indicated that effectively identifying users’ emotional responses is an important issue to improve the quality of ambient intelligence. In the current study, two bipolar facial electromyography (EMG) channels over corrugator supercilii and zygomaticus major were employed for differentiating various emotional states in two dimensions of valence (negative, neutral and positive) and arousal (high and low) while participants looked at affective visual stimuli. The results demonstrated that corrugator EMG and zygomaticus EMG efficiently differentiated negative and positive emotions from others, respectively. Moreover, corrugator EMG discriminated emotions on valence clearly, whereas zygomaticus EMG was ambiguous in neutral and negative emotional states. However, there was no significant statistical evidence for the discrimination of facial EMG responses in the dimension of arousal. Furthermore, correlation analysis proved significant correlations between facial EMG activities and ratings of valence performed by participants and other samples, which strongly supported the consistency of facial EMG reactions and subjective emotional experiences. In addition, the repeatability of facial EMG indicated by intraclass correlation coefficient (ICC) were provided, in which corrugator EMG held an excellent level of repeatability, and zygomaticus EMG grasped only a poor level of repeatability. Considering these results, facial EMG is reliable and effective to identify negative and positive emotional experiences elicited by affective visual stimuli, which may offer us an alternative method in building a basis for automated classification of users’ affective states in various situations.

The effect of forced choice on facial emotion recognition: a comparison to open verbal classification of emotion labels

Objective: This article includes the examination of potential methodological problems of the application of a forced choice response format in facial emotion recognition. Methodology: 33 subjects were presented with validated facial stimuli. The task was to make a decision about which emotion was shown. In addition, the subjective certainty concerning the decision was recorded. Results: The detection rates are 68% for fear, 81% for sadness, 85% for anger, 87% for surprise, 88% for disgust, and 94% for happiness, and are thus well above the random probability. Conclusion: This study refutes the concern that the use of forced choice formats may not adequately reflect actual recognition performance. The use of standardized tests to examine emotion recognition ability leads to valid results and can be used in different contexts. For example, the images presented here appear suitable for diagnosing deficits in emotion recognition in the context of psychological disorders and for mapping treatment progress.

Mapping discrete emotions into the dimensional space: An empirical approach

A critical task in Affective Computing is the reliable assessment of emotional states. The two most prominent approaches to classify emotions are categorical concepts of discrete emotions (e.g. OCC) and dimensional models typically using the pleasure - arousal - dominance space (PAD). In current research and applications, however, there is little overlap between these two concepts. A mapping of discrete categories into the dimensional space would offer new possibilities to model the emotional states of users and artificial agents, though. We hence let N=70 healthy subjects place the labels of discrete OCC emotions into PAD space according to their subjective knowledge with a simple visual tool. There was a high inter-subject consistency regarding the positioning of OCC emotions for the dimension of pleasure. However, arousal and dominance ratings showed considerably greater variance. We conclude that global and reliable mappings of OCC emotions into the PAD space can best be provided for the pleasure dimension. The exact positioning of discrete emotions regarding arousal and dominance can only be gained by individual calibration of a given user in a strict within-subject approach.

The influence of neuroticism and psychological symptoms on the assessment of images in three-dimensional emotion space.

Objective: The present study investigated the influence of neuroticism (NEO Five-Factor Inventory (NEO-FFI)) and psychological symptoms (Brief Symptom Inventory (BSI)) on pleasure, arousal, and dominance (PAD) ratings of the International Affective Picture System (IAPS). Methods: The subjects (N=131) were presented with images from the IAPS (30 images) and new images (30 images). The influence of neuroticism and BSI (median split: high vs. low) on the assessment of pleasure, arousal and dominance of the images was examined. Correlations of pleasure, arousal and dominance were presented in a 3-D video animation. Results: Subjects with high scores (compared to subjects with low scores by median split) of neuroticism and psychological symptoms of the BSI rated the presented emotional images more negative in the valence dimension (pleasure), higher in arousal and less dominant. Conclusion: Neuroticism and psychological symptoms influence the subjective emotional evaluation of emotional images. Therefore the location in the three-dimensional emotion space depends on individual differences. Such differences must be kept in mind, if correlations between emotion ratings and other variables like psychobiological measures are analyzed.

EEG: pattern classification during emotional picture processing

In this work we describe the processing and classifying of EEG-data that was acquired under emotional conditions. In the context of assistive environment technology it is one of the most important challenges to get information about a persons emotional state. To get this information, psychophysiological data was recorded while stimulating subjects with emotional pictures. Afterwards a classifier was trained to differentiate between physiological patterns of negative, positive and neutral conditions. The classification results show an accuracy of about 72%.

The Influence of Naturalness, Attractiveness and Intensity on Facial Emotion Recognition

Understanding the determinants facial emotion recognition is still one of the main topics in emotion research. Mimic expressions are not only a representation of feelings caused by emotions, but are also an important communication channel in social interaction. Research of the last 20 years could show that emotion recognition abilities differ within and between individuals. Moreover, every basic emotion seems to be processed differently in the human brain. Underlying processes are still not clear. In this study, the common practice of presenting pictures of faces showing basic emotions to participants was used in a more methodological sense. Possible determining factors in facial emotion recognition, like naturalness and intensity of the expressed emotion and the attractiveness of the photographed person, were analyzed.

Individual emotional profiles in Wizard-of-Oz-experiments

One of the most important and difficult fields in research of assistive environment technology is the recognition of emotional and motivational users states. Emotion studies in the past show, that there are only a few universal interindividual valid psychobiological profiles states, which are stable associated with a users emotional state. In this approach we look for intraindividual valid psychobiological patterns of emotions and motivations. In order to predict such states separate for different subjects we introduced a calibration procedure for each of the 20 subjects. We expect higher emotion recognition rates than preceding studies focusing on universal patterns on sample data. First results will be presented at the conference.

Modeling Emotions in Simulated Computer-Mediated Human-Human Interactions in a Virtual Game Environment

Emotions form a major part of humans’ day-to-day lives, especially in the areas of communication and interaction with others. They modify our gesture or facial expression and therefore serve as an additional communication channel. Furthermore, they have an impact on decision-making. This has two possible implications for computer science in the field of human-computer-interaction. First, computers should be able to adequately recognize and model human emotions if they genuinely want to help users in applied fields of human-human interactions. Second, a reliable and valid computer model of users’ emotions is the basis of effective implementations for human-computer interaction, with the computer thus being able to adapt to users’ emotions flexibly in any given application.