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SSTM-IS: simplified STM method based on instance selection for real-time EEG emotion recognition.

Shuang Ran1, Wei Zhong2, Danting Duan1

  • 1Key Laboratory of Media Audio & Video, Ministry of Education, Communication University of China, Beijing, China.

Frontiers in Human Neuroscience
|June 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel instance selection strategy for transfer learning to enable real-time emotion recognition using electroencephalography (EEG) signals. The developed algorithm achieves high accuracy in short computation times, facilitating practical applications.

Keywords:
EEG signalsbrain-computer interfaceinstance selectionreal-time emotion recognitiontransfer learning

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Electroencephalography (EEG) signals offer non-invasive monitoring of brain activity, crucial for brain-computer interfaces (BCI).
  • Objective emotion recognition via EEG is a key research area, but existing systems often process data offline, limiting real-time application.
  • The dynamic nature of human emotions necessitates real-time processing capabilities in affective BCIs.

Purpose of the Study:

  • To address the limitations of offline processing in affective BCIs.
  • To develop a novel algorithm for real-time emotion recognition using EEG signals.
  • To improve the speed and accuracy of emotion recognition models for new subjects.

Main Methods:

  • Introduction of an instance selection strategy within transfer learning.
  • Proposal of a simplified style transfer mapping algorithm.
  • Informativ e instance selection from source domain data and simplified hyperparameter update strategies for faster, more accurate model training.

Main Results:

  • High recognition accuracies achieved on SEED (86.78%), SEED-IV (82.55%), and a custom offline dataset (77.68%).
  • Demonstrated rapid computation times: 7s for SEED, 4s for SEED-IV, and 10s for the offline dataset.
  • Successful development and integration of a real-time emotion recognition system encompassing EEG acquisition, processing, recognition, and visualization.

Conclusions:

  • The proposed algorithm accurately recognizes emotions in minimal time, fulfilling the requirements for real-time applications.
  • The instance selection and simplified style transfer mapping enhance model efficiency and effectiveness for new subjects.
  • Experimental results validate the algorithm's capability for practical, real-time emotion recognition using EEG.