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

Chang-Gyu Lee1, Joo Young Kim2

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The LLaMAC dataset uses biosignals and questionnaires to predict media success through emotion prediction. This approach enables analysis of emotions, liking, and familiarity from physiological data.

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Area of Science:

  • Affective computing
  • Human-computer interaction
  • Media psychology

Background:

  • Predicting media success is challenging.
  • Emotion plays a key role in media reception.
  • Biosignals offer objective measures of emotional states.

Purpose of the Study:

  • Introduce the LLaMAC dataset for emotion prediction in media.
  • Facilitate biosignal-based prediction of emotions and liking.
  • Enable correlation analysis between continuous and discrete emotional data.

Main Methods:

  • Collected biosignals (EEG, GSR, PPG, SKT, RESP) and questionnaire data from over 100 participants.
  • Validated biosignals using statistical metrics and signal-to-noise ratios.
  • Utilized Light Gradient Boosting Machine (LightGBM) for emotion classification.

Main Results:

  • Demonstrated the feasibility of predicting emotions and liking from biosignals.
  • Established correlations between continuous (valence, arousal, dominance) and discrete emotional states.
  • Identified differences in biosignals related to media familiarity.

Conclusions:

  • The LLaMAC dataset supports biosignal-based emotion and liking prediction.
  • Findings advance understanding of the relationship between physiological responses and media experience.
  • The dataset facilitates further research into affective computing and media engagement.