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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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Semi-Supervised Cross-Subject Emotion Recognition Based on Stacked Denoising Autoencoder Architecture Using a Fusion

Junhai Luo1, Yuxin Tian1, Hang Yu1

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China.

Entropy (Basel, Switzerland)
|May 28, 2022
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Summary

This study introduces a Stacked Denoising Autoencoder (SDA) for automatic emotion recognition using physiological signals. The SDA model effectively extracts affective representations, outperforming traditional hand-engineered features and other deep learning methods.

Keywords:
DEAP datasetelectroencephalogram (EEG)emotion recognitionmulti-source fusionstacked denoising autoencoderunsupervised representation learning

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Area of Science:

  • Physiological computing
  • Affective computing
  • Machine learning for emotion recognition

Background:

  • Emotion recognition research increasingly focuses on physiological patterns.
  • Existing methods often rely on manually designed features from physiological data.
  • Developing automated methods for robust emotion representation is crucial.

Purpose of the Study:

  • To propose and evaluate an automated feature extraction method for emotion recognition using Stacked Denoising Autoencoder (SDA).
  • To compare the performance of SDA-generated features against hand-engineered features and other deep learning architectures.
  • To investigate the effectiveness of fusing electroencephalogram (EEG) and peripheral physiological signals for emotion detection.

Main Methods:

  • Utilized a Stacked Denoising Autoencoder (SDA) with unsupervised pre-training and supervised fine-tuning for automatic feature learning.
  • Performed binary classification tasks based on the Valence-Arousal-Dominance (VAD) emotion model.
  • Implemented data-level fusion for deep learning methods and decision/feature fusion for hand-engineered features.
  • Compared SDA against two other deep architectures and a generative stacked semi-supervised architecture.

Main Results:

  • Fusion of physiological signals (EEG and peripheral) generally outperformed individual modalities.
  • The proposed SDA scheme demonstrated slightly superior performance compared to other deep feature extractors.
  • The SDA method surpassed the performance of state-of-the-art hand-engineered features in emotion recognition tasks.

Conclusions:

  • Automated feature extraction using SDA offers an effective alternative to manual feature engineering in emotion recognition.
  • Deep learning approaches, particularly SDA with fused physiological data, show significant promise for advancing emotion recognition technology.
  • The findings support the potential of SDA for developing more accurate and robust systems for detecting emotional states from physiological signals.