This paper discusses representation learning from electroencephalographic (EEG) signal with deep predictive coding networks. We introduce a hierarchical probabilistic network that minimises prediction error on multiple levels. While the lowest layer predicts brain activity directly, higher layers abstract away from the data and predict sequences of the hidden states in lower layers. The network captures both expected and actual uncertainty by relating predicted and observed mean and variance of the state posteriors. Each layer minimises (expected) surprise either with or without sampling new evidence from the layer below. This structure motivates both active learning and active inference as means to learn representations. Active learning refers to model parameter exploration which allows to learn regularities, especially when they are stable between trials. Active inference refers to hidden state exploration, a process that enables dynamic inference of the current context using the learned generative model. We show that separating and weighting the internally propagated errors from those used for model weight updates allows to define prediction errors independently for each learning type. We train the model on EEG data recorded during free reading and apply it to adaptive Fixation Related Potential (FRP) prediction.