Recently, deep learning models have been successfully employed in many video-based affective computing applications (e.g., detecting pain, stress, and Alzheimer’s disease). One key application is automatic depression recognition – recogni…
Physiological responses to persuasion can help to increase our understanding of persuasive processes, and thereby the effectiveness of persuasive interventions. However, a clear relationship between psychophysiology and persuasion is not yet establishe…
In this article, we investigate brain directed connectivity (BDC) networks for emotion recognition using electroencephalogram (EEG) source signals that were estimated from high-density sensor EEG signals, for the first time. Currently, a variety of fea…
Although speech driven facial animation has been studied extensively in the literature, works focusing on the affective content of the speech are limited. This is mostly due to the scarcity of affective audio-visual data. In this article, we improve th…
The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. In this article, we propose and validate a novel implicit measure to predict an individual’s levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Participants viewed Chinese words with positive, negative, and neutral emotions. The multi-channel event-related potentials elicited by these emotional words were used to train a sparse regression model for personality prediction. Results from a large test sample of 196 participants indicated that the personality scores derived from the proposed measure reached significant correlations with a commonly used questionnaire (r = .50, .60, .49, .55, and .49 for agreeableness, conscientiousness, neuroticism, openness, and extraversion, respectively). The EEG-based personality scores showed good external validity as well, capable of predicting behavioral indices and psychological adjustment similar to self-reported scores. Besides, the EEG-based scores were relatively stable across time, as reflected by the test-retest reliability of .5 ∼ .7 for the five personality traits within a cohort of 33 participants 19-78 days later. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice.
Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract…
We exploit a self-supervised deep multi-task learning framework for electrocardiogram (ECG) -based emotion recognition. The proposed solution consists of two stages of learning a) learning ECG representations and b) learning to classify emotions. ECG r…
Automatic Dynamic Facial Expression Recognition (DFER) is a challenging task, since how to effectively capture facial temporal dynamics is still an open problem. In this article, we regard variations of facial expressions as a dynamic system in accord …
Humans have the ability to convey an array of emotions through complex and rich touch gestures. However, it is not clear how these touch gestures can be reproduced through interactive systems and devices in a remote mediated communication context. In t…
Emotions are an inseparable part of human nature affecting our behavior in response to the outside world. Although most empirical studies have been dominated by two theoretical models including discrete categories of emotion and dichotomous dimensions,…