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EEG-fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network.

Guijun Chen1, Yue Liu1, Xueying Zhang1

  • 1College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Brain Sciences
|August 29, 2024
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Summary
This summary is machine-generated.

This study introduces a novel Graph Convolution and Capsule Attention Network (GCN-CA-CapsNet) for improved emotion recognition using electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) data. The model enhances feature fusion and learning, increasing accuracy by 3-11%.

Keywords:
capsule attention networkelectroencephalogram (EEG)emotion recognitionfunctional near-infrared spectroscopy (fNIRS)graph convolution network

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are objective measures for assessing emotional states and are crucial in emotion recognition research.
  • Effective feature fusion and discriminative learning from combined EEG-fNIRS data remain a significant challenge in enhancing emotion recognition accuracy.

Purpose of the Study:

  • To propose and validate a novel Graph Convolution and Capsule Attention Network (GCN-CA-CapsNet) model for improved emotion recognition using multimodal EEG-fNIRS data.
  • To address the challenges of feature fusion and learning in multimodal brain-computer interfaces for affective computing.

Main Methods:

  • Collected simultaneous EEG-fNIRS signals from 50 subjects exposed to emotional video stimuli.
  • Employed graph convolution with a Pearson correlation adjacency matrix for fusing EEG-fNIRS features into primary capsules.
  • Utilized a capsule attention module and dynamic routing for selecting high-quality capsules and generating robust classification capsules.

Main Results:

  • The proposed GCN-CA-CapsNet model demonstrated superior performance compared to existing state-of-the-art methods.
  • Ablation studies validated the efficacy of the GCN-CA-CapsNet approach on a custom emotional EEG-fNIRS dataset.
  • The method achieved an average accuracy increase of 3-11% in emotion recognition.

Conclusions:

  • The GCN-CA-CapsNet model effectively integrates EEG and fNIRS data for enhanced emotion recognition.
  • The proposed attention mechanism and graph convolution approach significantly improve discriminative feature learning.
  • This work offers a promising advancement in multimodal brain-computer interfaces for affective computing and emotion recognition.