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Related Experiment Video

Updated: Sep 15, 2025

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
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Electroencephalogram-Based Satisfaction Assessment Brain-Computer Interface in Emerging Video Service by Using Graph

Yifan Niu1, Ziyu Li2, Gangyan Zeng3

  • 1School of Artificial Intelligence, Beijing Normal University, Beijing, China.

Brain Connectivity
|July 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel electroencephalogram (EEG) method for quantitatively assessing user satisfaction with emerging video services (EVS). The Point-to-Global (P2G) strategy effectively models complex brain states, outperforming existing techniques.

Keywords:
brain–computer interfaceelectroencephalogramemerging video servicegraph representation learningsatisfaction

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Quantitative assessment of user satisfaction in Emerging Video Services (EVS) is critical but challenging with traditional methods.
  • Electroencephalogram (EEG) offers objective brain activity insights, making it a promising tool for EVS satisfaction modeling.
  • Existing EEG approaches struggle to represent complex cognitive functions like emotion and perception involved in EVS satisfaction.

Purpose of the Study:

  • To investigate the utility of EEG for quantitative satisfaction assessment in EVS.
  • To develop an advanced EEG-based brain-computer interface (BCI) for modeling EVS satisfaction.
  • To address the challenge of representing complex brain states in EEG data.

Main Methods:

  • Proposed a Point-to-Global (P2G) graph representation learning strategy for EEG-based EVS satisfaction assessment.
  • Implemented a parallel coding module and a graph-based brain region perception module within the P2G strategy.
  • Captured satisfaction-sensitive graph representations by integrating point features and global topography from EEG signals.

Main Results:

  • Validated the P2G strategy's effectiveness on both self-constructed and public datasets.
  • Demonstrated that the P2G method outperforms existing approaches in EVS satisfaction modeling.
  • Identified specific neural markers associated with EVS satisfaction through detailed visual analysis.

Conclusions:

  • The P2G strategy offers a robust method for quantitative EEG-based EVS satisfaction assessment.
  • This research provides a scientific basis for optimizing and developing future video services.
  • EEG-based BCI holds significant potential for understanding and enhancing user experience in multimedia services.