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EESCN: A novel spiking neural network method for EEG-based emotion recognition.

FeiFan Xu1, Deng Pan1, Haohao Zheng1

  • 1Hangzhou Dianzi University, School of Computer Science and Technology, HangZhou, ZheJiang, China.

Computer Methods and Programs in Biomedicine
|November 24, 2023
PubMed
Summary

A new method, Emo-EEGSpikeConvNet (EESCN), significantly enhances electroencephalograph (EEG) emotion recognition accuracy and efficiency. This spiking neural network approach offers improved bio-interpretability and robustness for practical applications.

Keywords:
Convolutional neural network (CNN)EEG emotion recognitionNeuromorphicRecurrent neural network (RNN)Spiking neural network (SNN)

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Existing artificial neural networks show limitations in bio-interpretability and robustness for electroencephalograph (EEG) emotion recognition.
  • Advancements are needed to improve the efficiency and performance of EEG-based emotion recognition systems.

Purpose of the Study:

  • To develop a highly efficient and high-performance method for emotion recognition using EEG.
  • To enhance bio-interpretability and robustness in EEG emotion recognition.

Main Methods:

  • Proposed Emo-EEGSpikeConvNet (EESCN), a novel method utilizing a spiking neural network (SNN).
  • Developed a neuromorphic data generation module to convert EEG data into a 2D frame format.
  • Employed a NeuroSpiking framework for extracting spatio-temporal features from EEG for classification.

Main Results:

  • Achieved high emotion recognition accuracies: 94.56%-94.81% on the DEAP dataset and 79.65% on the SEED-IV dataset.
  • Demonstrated significant performance improvements compared to existing SNN methods for EEG emotion recognition.
  • EESCN exhibited faster running speeds and a smaller memory footprint.

Conclusions:

  • EESCN demonstrates excellent performance and efficiency in EEG-based emotion recognition.
  • The method shows potential for practical applications with portability and resource constraints.
  • The developed approach advances the field of brain-computer interfaces for emotion detection.