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Emotion plays a significant role in our daily life. Recognition of emotion iswide-spread in the field of health care and human-computer interaction. Emotionis the result of the coordinated activities of cortical and subcortical neuralprocesses, which correlate to specific physiological responses. However, theexisting emotion recognition techniques failed to combine various physiologicalsignals as one integrated feature representation. Meanwhile, many researchersignored the problem of over-fitting model with high accuracy, which wasactually false high accuracy caused by improper pre-processing. In this paper,sigmoid baseline filtering is conducted to solve the over-fitting problem fromsource. To construct a physiological-based algorithm, a 3D spatial andfunctional brain mapping is proposed based on human physiological mechanism andinternational electrode system, which combines the signals of the central andperipheral nervous system together. By combining the baseline filtering, 3Dbrain mapping, and simple 4D-CNN, a novel emotion recognition model is finallyproposed. Experiment results demonstrate that the performance of the proposedmodel is comparable to the state of art algorithms.

EEG-based Emotion Recognition with Spatial and Functional Brain Mapping of CNS and PNS Signals