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<p>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.</p>

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LLaMAC: low-cost biosignal sensor based large multimodal dataset for affective computing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Chang-Gyu Lee,
  • Joo Young Kim

摘要

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.