  In the field of affective computing, researchers in the community have
promoted the performance of models and algorithms by using the complementarity
of multimodal information. However, the emergence of more and more modal
information makes the development of datasets unable to keep up with the
progress of existing modal sensing equipment. Collecting and studying
multimodal data is a complex and significant work. In order to supplement the
challenge of partial missing of community data. We collected and labeled a new
homogeneous multimodal gesture emotion recognition dataset based on the
analysis of the existing data sets. This data set complements the defects of
homogeneous multimodal data and provides a new research direction for emotion
recognition. Moreover, we propose a pseudo dual-flow network based on this
dataset, and verify the application potential of this dataset in the affective
computing community. The experimental results demonstrate that it is feasible
to use the traditional visual information and spiking visual information based
on homogeneous multimodal data for visual emotion recognition.The dataset is
available at \url{https://github.com/201528014227051/SGED}
