Ulm-TSST dataset - raw data (MuSe2021)

Ulm-TSST dataset of the 2nd Multimodal Sentiment Challenge! 
The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines. We introduce the novel dataset Ulm-TSST database, supplying a multimodal richly annotated dataset of external dimensional ratings of emotion and mental well-being. After a brief period of preparation the subjects are asked to give an oral presentation, within a job-interview setting.  Ulm-TSST includes biological recordings, such as Electrocardiogram (ECG),  Electrodermal Activity (EDA), Respiration, and Heart Rate (BPM) as well as continuous arousal and valence annotations. With 105 participants (69.5% female) aged between 18 and 39 years, a total of 10 hours were accumulated.