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Spatial-temporal features-based EEG emotion recognition using graph convolution network and long short-term memory.

Fa Zheng1,2, Bin Hu1,2, Xiangwei Zheng1,2,3

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan, People's Republic of China.

Physiological Measurement
|May 17, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces ERGL, a novel method for emotion recognition using electroencephalography (EEG) signals. ERGL effectively captures spatial-temporal features, achieving high accuracy in classifying emotional states.

Keywords:
Electroencephalography (EEG)emotion recognitiongraph convolution network (GCN)long short-term memory (LSTM)

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction (HCI)
  • Neuroscience
  • Machine Learning

Background:

  • Emotion recognition from electroencephalography (EEG) signals is crucial for cognitive science and HCI.
  • Existing methods often neglect the spatial relationships between EEG channels or focus solely on time-frequency features.
  • There is a need for advanced techniques that integrate both spatial and temporal information from EEG data.

Purpose of the Study:

  • To develop a novel EEG-based emotion recognition method, ERGL, that effectively utilizes spatial-temporal features.
  • To improve the accuracy and robustness of emotion classification by incorporating spatial correlations between EEG channels.
  • To address the limitations of existing methods by combining graph convolution networks (GCN) and long short-term memory (LSTM) for feature extraction.

Main Methods:

  • Converted one-dimensional EEG signals into a two-dimensional mesh matrix to represent spatial correlations between adjacent channels.
  • Employed a Graph Convolutional Network (GCN) to extract spatial features from the EEG data.
  • Utilized Long Short-Term Memory (LSTM) units to capture temporal dynamics, followed by a softmax layer for emotion classification.

Main Results:

  • Achieved high classification accuracies for valence and arousal dimensions on the DEAP dataset (up to 90.67% and 90.33%, respectively).
  • Demonstrated strong performance on the SEED dataset, with accuracy, precision, and F-score reaching 94.92%, 95.34%, and 94.17% for positive, neutral, and negative emotions.
  • The ERGL method showed significant improvements compared to existing state-of-the-art EEG emotion recognition techniques.

Conclusions:

  • The proposed ERGL method effectively integrates spatial and temporal features from EEG signals for accurate emotion recognition.
  • The GCN-LSTM architecture provides a powerful framework for capturing complex patterns in EEG data related to emotional states.
  • ERGL represents a promising advancement in the field of affective computing and HCI.