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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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Enhancing local representation learning through global-local integration with functional connectivity for EEG-based

Baole Fu1, Xiangkun Yu2, Guijie Jiang3

  • 1School of Automation, Qingdao University, Qingdao 266071, China; Institute for Future, Qingdao University, Qingdao 266071, China.

Computers in Biology and Medicine
|July 17, 2024
PubMed
Summary

This study introduces a new method for emotion recognition using electroencephalogram (EEG) signals by integrating global and local brain activity. The approach significantly improves accuracy in identifying emotional states from brainwave data.

Keywords:
Convolutional neural networkEEG embeddingEmotion recognitionFeature couplingFunctional connectivityGlobal–local integration

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Emotion recognition from electroencephalogram (EEG) signals is vital for understanding human affective states.
  • Existing methods face limitations in extracting and representing local EEG features, hindering comprehensive emotional information capture.
  • The need for advanced techniques to enhance local feature representation in EEG-based emotion recognition is evident.

Purpose of the Study:

  • To propose a novel approach for EEG-based emotion recognition by enhancing local representation learning through global-local integration.
  • To leverage functional connectivity to divide EEG signals into global and local embeddings for comprehensive and dynamic brain activity analysis.
  • To improve the accuracy and representation capabilities of emotion recognition models.

Main Methods:

  • A convolutional feature extraction branch using a residual network was designed to extract local features from global embeddings.
  • A multidimensional collaborative attention (MCA) module was introduced to further enhance the representation ability and accuracy of local features.
  • A feature coupling module (FCM) integrated local features and patch-embedded local embeddings using hierarchical connections and enhanced cross-attention for improved local representation learning.

Main Results:

  • The proposed method demonstrated superior performance in emotion recognition tasks across three public datasets.
  • Accuracy improvements were observed: 4.92% on DEAP, 1.11% on SEED, and 7.76% on SEED-IV compared to existing methods.
  • The global-local integration with functional connectivity effectively enhanced local representation learning.

Conclusions:

  • The novel approach significantly advances EEG-based emotion recognition by effectively integrating global and local brain activity patterns.
  • The proposed method, utilizing functional connectivity and advanced attention mechanisms, offers a more comprehensive understanding of affective states.
  • This research provides a promising direction for developing more accurate and robust emotion recognition systems.