  Emotion plays a significant role in our daily life. Recognition of emotion is
wide-spread in the field of health care and human-computer interaction. Emotion
is the result of the coordinated activities of cortical and subcortical neural
processes, which correlate to specific physiological responses. However, the
existing emotion recognition techniques failed to combine various physiological
signals as one integrated feature representation. Meanwhile, many researchers
ignored the problem of over-fitting model with high accuracy, which was
actually false high accuracy caused by improper pre-processing. In this paper,
sigmoid baseline filtering is conducted to solve the over-fitting problem from
source. To construct a physiological-based algorithm, a 3D spatial and
functional brain mapping is proposed based on human physiological mechanism and
international electrode system, which combines the signals of the central and
peripheral nervous system together. By combining the baseline filtering, 3D
brain mapping, and simple 4D-CNN, a novel emotion recognition model is finally
proposed. Experiment results demonstrate that the performance of the proposed
model is comparable to the state of art algorithms.
