ArticleScientific data2025
LLaMAC: low-cost biosignal sensor based large multimodal dataset for affective computing.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- LLaMAC: low-cost biosignal sensor based large multimodal dataset for affective computing.Scientific data · 2025Article
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Authors and funding
2 authors.
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Abstract
The LLaMAC dataset was developed to predict the success of audio-visual media via emotion prediction. It was created using low-cost biosignal sensors, with emotional questionnaires in both continuous (valence, arousal, dominance) and discrete domains (emotion type and intensity: neutral, fun, sadness, anger, fear), and included over 100 participants. Questionnaires on liking and familiarity were also collected. The dataset contains five biosignals-EEG, GSR, PPG, SKT, and RESP-and seven questionnaire measures. Biosignals were validated using statistical metrics and signal-to-noise ratios, while questionnaire responses were assessed with scatter plots and statistical analyses. Emotion classification was performed using a Light Gradient Boosting Machine (LightGBM). The dataset enables biosignal-based prediction of emotions and liking, correlation analysis between continuous and discrete emotions, and investigation of biosignal differences related to familiarity, which can further inform emotion and liking predictions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.