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Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables
The Emognition dataset is dedicated to testing methods for emotion recognition (ER) from physiological responses and facial expressions. We collected data from 43 participants who watched short film clips eliciting nine discrete emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust,...
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| Published in: | Scientific data 2022-04, Vol.9 (1), p.158-158, Article 158 |
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| creator | Saganowski, Stanisław Komoszyńska, Joanna Behnke, Maciej Perz, Bartosz Kunc, Dominika Klich, Bartłomiej Kaczmarek, Łukasz D. Kazienko, Przemysław |
| description | The Emognition dataset is dedicated to testing methods for emotion recognition (ER) from physiological responses and facial expressions. We collected data from 43 participants who watched short film clips eliciting nine discrete emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, and sadness. Three wearables were used to record physiological data: EEG, BVP (2x), HR, EDA, SKT, ACC (3x), and GYRO (2x); in parallel with the upper-body videos. After each film clip, participants completed two types of self-reports: (1) related to nine discrete emotions and (2) three affective dimensions: valence, arousal, and motivation. The obtained data facilitates various ER approaches, e.g., multimodal ER, EEG- vs. cardiovascular-based ER, discrete to dimensional representation transitions. The technical validation indicated that watching film clips elicited the targeted emotions. It also supported signals’ high quality.
Measurement(s)
cardiac output measurement • Electroencephalography • Galvanic Skin Response • Temperature • acceleration • facial expressions
Technology Type(s)
photoplethysmogram • electroencephalogram (5 electrodes) • electrodermal activity measurement • Sensor • Accelerometer • Video Recording
Sample Characteristic - Organism
Homo sapiens
Sample Characteristic - Environment
laboratory environment |
| doi_str_mv | 10.1038/s41597-022-01262-0 |
| format | article |
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Measurement(s)
cardiac output measurement • Electroencephalography • Galvanic Skin Response • Temperature • acceleration • facial expressions
Technology Type(s)
photoplethysmogram • electroencephalogram (5 electrodes) • electrodermal activity measurement • Sensor • Accelerometer • Video Recording
Sample Characteristic - Organism
Homo sapiens
Sample Characteristic - Environment
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Measurement(s)
cardiac output measurement • Electroencephalography • Galvanic Skin Response • Temperature • acceleration • facial expressions
Technology Type(s)
photoplethysmogram • electroencephalogram (5 electrodes) • electrodermal activity measurement • Sensor • Accelerometer • Video Recording
Sample Characteristic - Organism
Homo sapiens
Sample Characteristic - Environment
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We collected data from 43 participants who watched short film clips eliciting nine discrete emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, and sadness. Three wearables were used to record physiological data: EEG, BVP (2x), HR, EDA, SKT, ACC (3x), and GYRO (2x); in parallel with the upper-body videos. After each film clip, participants completed two types of self-reports: (1) related to nine discrete emotions and (2) three affective dimensions: valence, arousal, and motivation. The obtained data facilitates various ER approaches, e.g., multimodal ER, EEG- vs. cardiovascular-based ER, discrete to dimensional representation transitions. The technical validation indicated that watching film clips elicited the targeted emotions. It also supported signals’ high quality.
Measurement(s)
cardiac output measurement • Electroencephalography • Galvanic Skin Response • Temperature • acceleration • facial expressions
Technology Type(s)
photoplethysmogram • electroencephalogram (5 electrodes) • electrodermal activity measurement • Sensor • Accelerometer • Video Recording
Sample Characteristic - Organism
Homo sapiens
Sample Characteristic - Environment
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| source | Publicly Available Content Database; PubMed Central; Springer Nature - nature.com Journals - Fully Open Access |
| subjects | 639/705/117 706/689/477 Anger Arousal Data Descriptor EEG Emotional behavior Emotions Emotions - physiology Facial Expression Galvanic skin response Humanities and Social Sciences Humans Motivation multidisciplinary Pattern recognition Physiology Sadness - psychology Science Science (multidisciplinary) Self Report |
| title | Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables |
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