During the last decades, information about the emotional state of users has become more and more important in human-computer interaction. Automatic emotion recognition enables the computer to recognize a user's emotional state and thus allows for appropriate reaction, which may pave the way for computers to act emotionally in the future. In the current study, we investigate different feature sets to build an emotion recognition system from electroencephalographic signals. We used pictures from the International Affective Picture System to induce three emotional states: pleasant, neutral, and unpleasant. We designed a headband with four build-in electrodes at the forehead, which was used to record data from five subjects. Compared to standard EEG-caps, the headband is comfortable to wear and easy to attach, which makes it more suitable for everyday life conditions. To solve the recognition task we developed a system based on support vector machines. With this system we were able to achieve an average recognition rate up to 66.7% on subject dependent recognition, solely based on EEG signals.
We describe a psychophysiological study of the emotion regulation of investment bank traders. Building on work on the role of emotions in financial decision making, we examined the relationship between market conditions, trader experience, and emotion regulation while trading, as indexed by high-frequency heart rate variability (HF HRV). We found a significant inverse relationship between HF HRV and market volatility and a positive relationship between HF HRV and trader experience. We argue that this suggests that emotion regulation may be an important facet of trader expertise, and that learning effects demonstrated in financial markets may include improved emotion regulation as an important component of that learning. Our results also suggest the value of investigating the role of effective emotion regulation in a broader range of financial decision-making contexts.
The objective of this paper is to examine the possibilities and limitations of heart rate variability (HRV) as an indicator of emotional arousal for mobile applications which require online biofeedback. In contrast to offline classification, feature extraction for online applications sets other requirements to the window size in which data is analyzed as the delay between a change of a person's arousal level and the reaction of an application should be as short as possible. For this purpose we compare various HRV features in order to evaluate how far window size can be decreased to enable online arousal recognition. Using data from a study where high and low arousal were induced in a game scenario, HRV features are analyzed for their discriminatory power depending on the window size using Fisher's discriminant analysis. Moreover, we use these features to train an SVM based classifier. Results indicate that for some features it is possible to use ultra-short term window sizes, i.e. window sizes shorter than the 5 minute window which has traditionally been used for short term HRV analysis.
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