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Emotion recognition framework based on adaptive window selection and CA-KAN.

Xuefen Lin1, Linhui Fan1, Yifan Gu2

  • 1College of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023 China.

Cognitive Neurodynamics
|June 27, 2025
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Summary
This summary is machine-generated.

This study introduces a novel framework for electroencephalogram (EEG)-based emotion recognition, achieving high accuracy. The method combines adaptive window selection with an enhanced neural network for efficient and reliable emotion detection.

Keywords:
Adaptive window selectionEEGEmotion recognitionKolmogorov-Arnold networks

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Emotion recognition using electroencephalogram (EEG) data is crucial for various applications.
  • Improving EEG data processing and emotion recognition models is an active research area.

Purpose of the Study:

  • To develop an advanced framework for EEG-based emotion recognition.
  • To enhance the accuracy and efficiency of emotion recognition models.

Main Methods:

  • Utilizing the CUSUM algorithm for adaptive window selection to extract emotion-relevant EEG segments.
  • Employing a convolutional attention-enhanced Kolmogorov-Arnold Network (CA-KAN) for robust emotion classification.

Main Results:

  • Achieved peak classification accuracies of 94.63% on the SEED dataset and 94.73% on the SEED-IV dataset.
  • Demonstrated a lightweight framework suitable for real-world deployment.

Conclusions:

  • The proposed CA-KAN framework significantly improves EEG-based emotion recognition accuracy and efficiency.
  • The framework shows potential for applications like medical emotion monitoring and driver emotion detection.