Emotions play a significant role in the cognitive processes of the human
brain, such as decision making, learning and perception. The use of
physiological signals has shown to lead to more objective, reliable and
accurate emotion recognition combined with raising machine learning methods.
Supervised learning methods have dominated the attention of the research
community, but the challenge in collecting needed labels makes emotion
recognition difficult in large-scale semi- or uncontrolled experiments.
Unsupervised methods are increasingly being explored, however sub-optimal
signal feature selection and label identification challenges unsupervised
methods' accuracy and applicability. This article proposes an unsupervised deep
cluster framework for emotion recognition from physiological and psychological
data. Tests on the open benchmark data set WESAD show that deep k-means and
deep c-means distinguish the four quadrants of Russell's circumplex model of
affect with an overall accuracy of 87%. Seeding the clusters with the subject's
subjective assessments helps to circumvent the need for labels.

情绪在人脑的认知过程中扮演重要角色，本研究提出了一种基于生理和心理数据的无监督深度聚类框架用于情绪识别。通过对 WESAD 数据集的测试，深度 k 均值和深度 c 均值在 Russell 情感模型的四个象限上具有 87% 的总体准确率，并通过主观评估结果初始化聚类，避免了需要标签的问题。