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This review synthesizes advanced signal processing and deep learning methods for emotion recognition using electrodermal activity (EDA). It highlights the need for rigorous methods to accurately interpret physiological signals for affective states.

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Area of Science:

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

Background:

  • Electrodermal activity (EDA) is crucial for emotion recognition due to its link with sympathetic nervous system activation.
  • Limited systematic research exists on signal processing techniques tailored for EDA in emotion recognition.
  • Methodological rigor is needed to define emotions and link physiological manifestations (EDA) to affective states.

Purpose of the Study:

  • To review advanced EDA signal processing methods and deep learning (DL) approaches for emotion recognition (2018-2025).
  • To adopt a signal-processing-centric and physiology-informed perspective, contrasting with system-level design focus of prior surveys.
  • To provide a structured comparative evaluation of EDA decomposition methods for a unified framework.

Main Methods:

  • Systematic review of literature on EDA signal processing and DL for emotion recognition.
  • Comparison of time-domain, frequency-domain, time-frequency, and advanced time-series analysis techniques.
  • Evaluation of emerging end-to-end DL architectures and EDA decomposition methods using performance metrics.

Main Results:

  • Identified challenges in EDA acquisition, including non-stationarity and inter-subject variability.
  • Compared various signal processing techniques and DL architectures for affective modeling.
  • Provided a structured evaluation of EDA decomposition methods.

Conclusions:

  • Hybrid approaches combining classical signal processing interpretability with DL predictive power are advocated.
  • The review offers a comprehensive resource for researchers and practitioners in EDA-based emotion recognition.
  • Emphasizes the importance of signal processing choices for accuracy, robustness, and interpretability in emotion recognition systems.