Electroencephalography (EEG) is a major tool for studying neurophysiological processes. Investigating reliable representations from highly noisy measurements is a pending challenge, however, the medically treasured and insufficient labeled data have driven this process away from a supervised learning manner. Recent works have turned their attention to self-supervised learning (SSL), putting the contrastive strategy on capturing the spatio-temporal characteristics of the neuronal events of interest. We argue that the temporal-spatial view is not the best choice for the SSL contrastive objective because there is a missing piece of the EEG representation that is usually ignored: dynamic fluctuations in brain neurons and the statistical learning of analog/artificial neural networks cannot handle the dynamic characteristics well. This paper proposes a novel two-view contrastive learning framework to refine EEG features from local-global and past-future views. An array of spiking neural networks is embedded to project spatio-temporal features onto the spike sequences to represent the dynamic fluctuation information of EEG. Experimenting with sleep stage classification and prediction of lethal epileptic seizures, we verify the proposal competes favorably against the state-of-the-art methods and offers high-quality features, that is, supervised learning on top of them observes a significant improvement in classification after only one training iteration.