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Semi-supervised bipartite graph construction with active EEG sample selection for emotion recognition.

Bowen Pang1, Yong Peng2,3, Jian Gao4

  • 1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.

Medical & Biological Engineering & Computing
|May 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for cross-subject emotion recognition using electroencephalogram (EEG) signals. The approach effectively handles individual differences by constructing a semi-supervised bipartite graph with active sample selection.

Keywords:
Active sample selectionBipartite graphElectroencephalogram (EEG)Emotion recognitionSemi-supervised learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Electroencephalogram (EEG) signals are increasingly used for emotion recognition due to their direct link to the central nervous system.
  • The non-stationary nature of EEG and inter-subject variability pose significant challenges for accurate cross-subject emotion recognition models.

Purpose of the Study:

  • To propose a novel semi-supervised bipartite graph construction with active EEG sample selection (SBGASS) method for robust cross-subject emotion recognition.
  • To address the limitations of inter-subject variability and negative samples in EEG-based emotion recognition.

Main Methods:

  • SBGASS adaptively learns a bipartite graph to connect labeled and unlabeled EEG samples across subjects.
  • Active sample selection is employed to identify and reject negative samples (outliers/noise) during graph construction.
  • The SEED-IV dataset was utilized for experimental validation.

Main Results:

  • SBGASS actively rejects negative labeled samples, improving bipartite graph construction and overall model performance.
  • Quantitative analysis of labeled EEG sample transferability revealed a decrease in transferability with increased distance from class centroids.
  • The study investigated spatial-frequency patterns in emotion recognition via an acquired projection matrix, alongside improved recognition accuracy.

Conclusions:

  • The proposed SBGASS method effectively mitigates inter-subject variability challenges in EEG-based emotion recognition.
  • Active sample selection is crucial for enhancing the robustness of bipartite graph construction by reducing the influence of noisy data.
  • The findings provide insights into sample transferability and spatial-frequency patterns, advancing the field of cross-subject emotion recognition.