# User independent Emotion Recognition with Residual Signal-Image Network

## TL;DR

This paper introduces Res-SIN, an end-to-end CNN framework that transforms decomposed EDA signals into images for large-scale, user-independent emotion classification, achieving over 73% accuracy on a new extensive dataset.

## Contribution

It presents the first large-scale, user-independent emotion recognition method using EDA signal images processed by CNNs, leveraging convex optimization and external benchmarks.

## Key findings

- Achieved 73.65% accuracy for arousal classification.
- Achieved 73.43% accuracy for valence classification.
- Validated on the largest EDA emotion dataset to date.

## Abstract

User independent emotion recognition with large scale physiological signals is a tough problem. There exist many advanced methods but they are conducted under relatively small datasets with dozens of subjects. Here, we propose Res-SIN, a novel end-to-end framework using Electrodermal Activity(EDA) signal images to classify human emotion. We first apply convex optimization-based EDA (cvxEDA) to decompose signals and mine the static and dynamic emotion changes. Then, we transform decomposed signals to images so that they can be effectively processed by CNN frameworks. The Res-SIN combines individual emotion features and external emotion benchmarks to accelerate convergence. We evaluate our approach on the PMEmo dataset, the currently largest emotional dataset containing music and EDA signals. To the best of author's knowledge, our method is the first attempt to classify large scale subject-independent emotion with 7962 pieces of EDA signals from 457 subjects. Experimental results demonstrate the reliability of our model and the binary classification accuracy of 73.65% and 73.43% on arousal and valence dimension can be used as a baseline.