Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications
This work addresses the challenge of extracting reliable emotional biomarkers from noisy wearable data for mental health monitoring, representing an incremental improvement with a novel deep learning approach.
This study tackled the problem of decomposing Electrodermal Activity (EDA) signals into phasic and tonic components for real-world mental health applications, introducing the Feel Transformer model which achieved a balance between feature fidelity and robustness to noisy data compared to existing methods.
Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting.