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In this study, we combined fMRI (functional Magnetic Resonance Imaging) data and Poincare plots derived from PPG (Photoplethysmography) to investigate the relationship between brain activity captured by fMRI and PPG signals and mood classification. The study focused on extracting features from the fMRI data and using these features along with the heart rate variability index derived from the Poincare plot to categorize mood. The study used the XGBOOST model for classification, and the accuracy varied depending on the model and mood classification. The results of this study suggest that the combination of fMRI and HRV features can be effective for mood classification.

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fMRI Activity Slop Feature with Poincare Plot for Emotion Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Po-Han Huang,
  • Yu-Chen Lin,
  • Shao-Huang Lu,
  • Shi-Yi Wu,
  • Cheng-Lun Tsai,
  • Kang-Ping Lin

摘要

In this study, we combined fMRI (functional Magnetic Resonance Imaging) data and Poincare plots derived from PPG (Photoplethysmography) to investigate the relationship between brain activity captured by fMRI and PPG signals and mood classification. The study focused on extracting features from the fMRI data and using these features along with the heart rate variability index derived from the Poincare plot to categorize mood. The study used the XGBOOST model for classification, and the accuracy varied depending on the model and mood classification. The results of this study suggest that the combination of fMRI and HRV features can be effective for mood classification.