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Related Experiment Video

Updated: Jul 3, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Transformer-based emotion recognition in interactive art: A multimodal neural approach.

Xiaowei Chen1, Azlan Abdul Aziz2, Zainuddin Ibrahim3

  • 1School of Arts, Zhejiang Shuren University, Hangzhou, China.

Plos One
|June 10, 2026
PubMed
Summary

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This study combined electroencephalography (EEG) and self-reports to analyze how interactive digital art impacts emotions. Delta-band EEG activity was key for emotional recovery, showing potential for affective computing.

Area of Science:

  • Affective neuroscience
  • Human-computer interaction
  • Digital aesthetics

Background:

  • Understanding emotional responses to interactive digital art is crucial for bridging affective neuroscience and HCI.
  • Previous research often used isolated EEG or self-report measures, limiting insights into temporal affective dynamics.
  • This study integrates neural oscillatory features from EEG with subjective affect scores for a comprehensive analysis.

Purpose of the Study:

  • To develop and validate a multimodal approach for modeling affective changes induced by interactive digital art.
  • To investigate the relationship between electroencephalography (EEG) band-specific oscillations and subjective emotional states.
  • To assess the efficacy of a Transformer-based deep learning model in predicting affective shifts.

Main Methods:

Related Experiment Videos

Last Updated: Jul 3, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Utilized a publicly available dataset with pre- and post-interaction EEG recordings and Positive and Negative Affect Scale (PANAS) scores.
  • Pre-processed EEG data using independent component analysis, bandpass filtering, and z-score normalization.
  • Trained a multi-output Transformer regression model to predict changes in positive and negative affect from EEG band-wise change features.

Main Results:

  • The Transformer model outperformed LSTM and Random Forest baselines, achieving R² = 0.162.
  • Delta-band oscillations were strongly associated with affective recovery.
  • Changes in beta and gamma activity correlated with increased positive affect, while negative affect significantly decreased post-interaction.

Conclusions:

  • EEG band-change features are effective for modeling affective variations in interactive art contexts.
  • The multimodal approach provides methodological insights for affective computing in digital environments.
  • Findings integrate emotion regulation theory, affective aesthetics, and deep learning for enhanced understanding of user experience.