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PR2DM: Position-aware robust reconstruction with diffusion model for emotion recognition From EEG.

Wenqiang Xu1, Yuzhu Lin1, Xueqian Wang1

  • 1School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian 116029, China.

Computer Methods and Programs in Biomedicine
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel diffusion model (PR²DM) to improve electroencephalography (EEG) emotion recognition by addressing data scarcity and noise. The method significantly enhances accuracy in classifying emotional states from EEG signals.

Keywords:
Adaptive convolutionElectroencephalographyEmotion recognitionPosition-awareRobust reconstruction

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Emotion recognition is crucial for applications like smart education and mental health.
  • Electroencephalography (EEG) offers an objective method for emotion assessment via brain activity.
  • EEG data scarcity and positional noise present significant challenges to accurate emotion recognition.

Purpose of the Study:

  • To develop a robust method for emotion recognition from EEG signals.
  • To address the limitations of data scarcity and electrode-position-dependent noise in EEG.
  • To improve the accuracy and reliability of EEG-based emotion recognition systems.

Main Methods:

  • Proposed position-aware robust reconstruction with diffusion model (PR²DM).
  • Integrated electrode positional encoding and multi-scale temporal feature fusion.
  • Developed a spatial-temporal adaptive convolutional network for precise classification.

Main Results:

  • Achieved high accuracy in valence and arousal classification on DEAP (95.01%, 95.62%) and DREAMER (84.77%, 87.30%) datasets.
  • Demonstrated effective reconstruction of noise-independent EEG features using topographic map analysis.
  • Ablation studies confirmed the significant contribution of each component to performance enhancement.

Conclusions:

  • PR²DM effectively mitigates EEG data scarcity and noise inconsistencies.
  • The proposed method significantly improves emotion recognition accuracy using EEG signals.
  • This approach offers a promising solution for reliable EEG-based emotion analysis.