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

## TL;DR

This paper introduces the CASE dataset, which provides real-time continuous emotion annotations and synchronized physiological signals during video viewing, enabling more accurate emotion analysis in realistic settings.

## Contribution

The paper presents a novel dataset with continuous emotion annotations and physiological recordings, along with a new joystick-based annotation interface for simultaneous valence and arousal reporting.

## Key findings

- Dataset includes data from 30 participants with synchronized physiological signals.
- Validation shows the dataset effectively captures emotional responses.
- Provides a new resource for emotion analysis research.

## Abstract

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, 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 made of ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature. 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.