LLaMAC: Low-cost Biosignal Sensor based Large Multimodal Dataset for Affective Computing

This dataset consists of biosignals such as electroencephalogram (EEG), electrodermal activity (EDA), photoplethysmogram (PPG), skin temperature (SKT), and respiration (RESP), and questionnaire results on continuous domain emotion (valence, arousal, and dominance), discrete domain emotion (type and intensity of emotion: neutral, fun, sadness, angry, and fear), liking, and familiarity. This dataset has the characteristics of using low-cost biosignal sensors, conducting questionnaires on continuous and discrete domain emotions, participating in more than 100 participants, and conducting questionnaires on liking and familiarity. This dataset can be used to predict continuous and discrete domain emotions based on biosignals, analyze correlations between continuous and discrete domain emotions, predict liking based on biosignals, and identify differences in biosignals according to familiarity and predict emotions or liking using them.