Autonomic peripheral activity is partly governed by brain autonomic centers. However, there is still a lot of uncertainties regarding the precise link between peripheral and central autonomic biosignals. Clarifying these links could have a profound impact on the interpretability, and thus usefulness, of peripheral autonomic biosignals captured with wearable devices. In this study, we take advantage of a unique dataset consisting of intracranial stereo-electroencephalography (SEEG) and peripheral biosignals acquired simultaneously for several days from four subjects undergoing epilepsy monitoring. Compared to previous work, we apply a deep neural network to explore high-dimensional nonlinear correlations between the cerebral brainwaves and variations in heart rate and electrodermal activity (EDA). Further, neural network explainability methods were applied to identify most relevant brainwave frequencies, brain regions and temporal information to predict a specific biosignal. Strongest brain-peripheral correlations were observed from contacts located in the central autonomic network, in particular in the alpha, theta and 52 to 58 Hz frequency band. Furthermore, a temporal delay of 12 to 14 s between SEEG and EDA signal was observed. Finally, we believe that this pilot study demonstrates a promising approach to mapping brain-peripheral relationships in a data-driven manner by leveraging the expressiveness of deep neural networks.