{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Saganowski S"],"funding":["Polish Ministry of Education and Science -- the CLARIN-PL Project","Narodowe Centrum Nauki","Narodowe Centrum Nauki (National Science Centre)"],"pagination":["158"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8989970"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["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 tra"],"journal":["Scientific data"],"pubmed_title":["Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables."],"pmcid":["PMC8989970"],"funding_grant_id":["2020/37/B/ST6/03806","2016/21/B/ST6/01463"],"pubmed_authors":["Saganowski S","Perz B","Behnke M","Klich B","Kazienko P","Kunc D","Komoszynska J","Kaczmarek LD"],"additional_accession":[]},"is_claimable":false,"name":"Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables.","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 tra","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Apr","modification":"2026-05-30T15:27:56.411Z","creation":"2025-04-04T10:02:01.754Z"},"accession":"S-EPMC8989970","cross_references":{"pubmed":["35393434"],"doi":["10.1038/s41597-022-01262-0"]}}