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EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution.

Xiaoliang Zhu1, Chen Liu1, Liang Zhao1

  • 1National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan 430079, China.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Self-Organized Graph Pseudo-3D Convolution Network (SOGPCN) for accurate electroencephalogram (EEG) emotion recognition. The SOGPCN method significantly improves emotion recognition accuracy by analyzing brain region interactions and time-frequency features.

Keywords:
3D convolutionEEGSEEDemotion recognitiongraph neural network

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Human emotions are complex psychophysiological responses.
  • Accurate emotion recognition is crucial for advancing human-computer interaction.
  • Electroencephalogram (EEG) signals offer authentic, objective, and reliable data for emotion recognition, attracting significant research interest.

Purpose of the Study:

  • To address limitations in current EEG emotion recognition methods concerning inter-regional information exchange and time-frequency feature extraction.
  • To propose a novel EEG emotion recognition network, the Self-Organized Graph Pseudo-3D Convolution Network (SOGPCN), integrating attention and spatiotemporal convolution.

Main Methods:

  • The SOGPCN method constructs a self-organizing map for each frequency band to capture distinct spatial relationships between electrodes.
  • Graph convolution is utilized to analyze inter-channel spatial relationships within these self-organizing maps.
  • Pseudo-three-dimensional convolution with partial dot product attention extracts temporal EEG features, and LSTM learns contextual information.

Main Results:

  • The SOGPCN method achieved high accuracy in emotion recognition on the SEED dataset.
  • Subject-dependent experiments yielded a recognition accuracy of 95.26%.
  • Subject-independent experiments resulted in a recognition accuracy of 94.22%, outperforming baseline methods.

Conclusions:

  • The proposed SOGPCN method demonstrates superior performance in EEG-based emotion recognition.
  • The novel approach effectively captures complex spatiotemporal dynamics and inter-regional brain information.
  • SOGPCN represents a significant advancement in the field of affective computing and human-computer interaction.