Recent trends in electrodermal activity signal processing and deep learning methods for emotion recognition

Clinical Snapshot

Key Findings

• Effect Size: Not reported

• Primary Outcome: Comparative evaluation of EDA decomposition methods using performance metrics

• Nnt Or Sensitivity: Performance metrics for emotion recognition accuracy mentioned but not quantified

Clinical Application

Technical review with potential applications in mental health monitoring and affective computing No specific relevance to Australian healthcare system, PBS, or TGA regulations mentioned Limited direct clinical application - primarily relevant for researchers developing emotion recognition systems

Abstract

Electrodermal activity (EDA) has emerged as one of the most informative physiological signals for emotion recognition due to its close association with sympathetic nervous system activation. Although research on affective computing has expanded rapidly in recent years, systematic investigations of signal-processing techniques specifically tailored to EDA remain limited. This gap is significant because the selection of decomposition and signal processing strategies directly influences the accuracy, robustness, and interpretability of emotion recognition systems. Furthermore, defining emotion itself and tracing its physiological manifestations through EDA, from raw measurements to recognized affective states, remains an open challenge that highlights the need for methodological rigour. In this review, we synthesise developments published between 2018 and 2025, with a dual focus on advanced EDA signal processing methods and deep learning (DL) approaches for emotion recognition. We review common EDA acquisition sites, discuss challenges arising from non-stationarity and inter-subject variability. The review systematically compares time-domain, frequency-domain, time-frequency, and advanced time-series analysis techniques, alongside emerging end-to-end DL architectures for affective modelling. Unlike prior surveys that emphasise system-level design, this work adopts a signal-processing-centric and physiology-informed perspective. Additionally, we provide a structured comparative evaluation of widely used EDA decomposition methods using key performance metrics, offering a unified framework for method selection and interpretation. Overall, this review serves as a comprehensive resource for researchers and practitioners while advocating hybrid approaches that integrate the interpretability of classical signal processing with the predictive power of predictive methods.

References

1. 1.Veeranki, Y. R., Rao, N. K., Posada-Quintero, H. F., & Swaminathan, R. (2026). Recent trends in electrodermal activity signal processing and deep learning methods for emotion recognition. Neuroscience. https://doi.org/10.1016/j.neuroscience.2026.03.009