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.

翻译：情感在我们的日常生活中起着重要作用。对情感的认知在保健和人体计算机互动领域广泛存在。情感是皮质和亚皮质神经过程协调活动的结果,这些活动与具体的生理反应有关。但是,现有的情感识别技术未能将各种生理信号作为一个综合特征的体现方式加以结合。与此同时,许多研究人员忽视了高度精准的超配模型问题,实际上由于不适当的预处理而导致的超精度过高。在本文中,对小类基线进行过滤以解决来源的过度适应问题。为了构建基于生理的算法,根据人的生理机制和国际电解系统提出了3D空间和功能脑图,将中央和外围神经系统的信号结合起来。通过将基线过滤、3D脑映射和简单的4D-CNN结合起来,终于提出了一个新的情感识别模型。实验结果表明,拟议的模型的性能与艺术算法相似。