Affective signal processing (ASP) : unraveling the mystery of emotions

Broek, Egon L. van den

doi:10.3990/1.9789036532433

Cited by 16 publications

(28 citation statements)

References 580 publications

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“…Boring and relaxing videos, as expected, elicit similar values of all features. These results are in line with previous research[5,6], where, e.g., HR and SCR were determined to be positively correlated to arousal. This effect can also been seen in our data, where the reported arousal levels (see bottom-left panel) for scary videos are higher than for the other videos.…”

supporting

confidence: 93%

A dataset of continuous affect annotations and physiological signals for emotion analysis

et al. 2019

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From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, a direct and real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were obtained from ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature sensors. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.

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confidence: 93%

A dataset of continuous affect annotations and physiological signals for emotion analysis

et al. 2019

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“…To this end, wearable sensors for measuring physiological descriptors of emotions (e.g., galvanic skin response, heart rate, etc.) are often used [1], [2], [3]. The data from these sensors need to be linked to the internal emotions experienced by the human, and this association step is still a largely unsolved problem.…”

Section: Introductionmentioning

confidence: 99%

“…The standard paradigm in a laboratory setting is to provide emotion-inducing stimuli (e.g., videos [2], music [4], and/or photos [4]) to the participants and measure their affective response using biosignals [1], [2], [5], speech signals [6], and/or computer-vision based approaches [5]. The ground truth for the subject's emotional experience is then either obtained through Likert-scale based post-stimuli questionnaires or often manually annotated using discrete emotion labels [3]. Both these rating methods are unsuitable when using dynamic stimuli (e.g., videos), as they do not consider the time-varying nature of emotions [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Both these rating methods are unsuitable when using dynamic stimuli (e.g., videos), as they do not consider the time-varying nature of emotions [4], [5]. Secondly, discrete emotion labels are also insufficient to define the strength of emotional experiences [3], [7].…”

Section: Introductionmentioning

confidence: 99%

“…This property makes joystick-based setups more intuitive to use, in turn also reducing the cognitive load associated with the annotation procedure. Secondly, the 2D setup of our framework allows users to provide a more comprehensive description of their emotional experience than is possible by annotating each dimension separately [3], [8], [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Continuous, Real-Time Emotion Annotation: A Novel Joystick-Based Analysis Framework

Sharma

Castellini

Stulp

et al. 2020

IEEE Trans. Affective Comput.

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Emotion labels are usually obtained via either manual annotation, which is tedious and time-consuming, or questionnaires, which neglect the time-varying nature of emotions and depend on human's unreliable introspection. To overcome these limitations, we developed a continuous, real-time, joystick-based emotion annotation framework. To assess the same, 30 subjects each watched 8 emotion-inducing videos. They were asked to indicate their instantaneous emotional state in a valence-arousal (V-A) space, using a joystick. Subsequently, five analyses were undertaken: (i) a System Usability Scale (SUS) questionnaire unveiled the framework's excellent usability; (ii) MANOVA analysis of the mean V-A ratings and (iii) trajectory similarity analyses of the annotations confirmed the successful elicitation of emotions; (iv) Change point analysis of the annotations, revealed a direct mapping between emotional events and annotations, thereby enabling automatic detection of emotionally salient points in the videos; and (v) Support Vector Machines (SVM) were trained on classification of 5 second chunks of annotations as well as their change-points. The classification results confirmed that ratings patterns were cohesive across the participants. These analyses confirm the value, validity, and usability of our annotation framework. They also showcase novel tools for gaining greater insights into the emotional experience of the participants.

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Feedback beyond accuracy: Using eye‐tracking to detect comprehensibility and interest during reading

Sluis

Broek

2022

Asso for Info Science & Tech

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Knowing what information a user wants is a paramount challenge to information science and technology. Implicit feedback is key to solving this challenge, as it allows information systems to learn about a user's needs and preferences. The available feedback, however, tends to be limited and its interpretation shows to be difficult. To tackle this challenge, we present a user study that explores whether tracking the eyes can unpack part of the complexity inherent to relevance and relevance decisions. The eye behavior of 30 participants reading 18 news articles was compared with their subjectively appraised comprehensibility and interest at a discourse level. Using linear regression models, the eye-tracking signal explained 49.93% (comprehensibility) and 30.41% (interest) of variance (p < .001). We conclude that eye behavior provides implicit feedback beyond accuracy that enables new forms of adaptation and interaction support for personalized information systems.

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Affective signal processing (ASP) : unraveling the mystery of emotions

Cited by 16 publications

References 580 publications

A dataset of continuous affect annotations and physiological signals for emotion analysis

A dataset of continuous affect annotations and physiological signals for emotion analysis

Continuous, Real-Time Emotion Annotation: A Novel Joystick-Based Analysis Framework

Feedback beyond accuracy: Using eye‐tracking to detect comprehensibility and interest during reading

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