High Frequency Oscillations (HFOs, 200-600 Hz) are recognized as a biomarker of epileptogenic brain areas. This work aims at designing novel microelectrodes in order to optimize the recording and further detection of HFOs in brain (intracerebral electroencephalography, iEEG). The quality of the recorded iEEG signals is highly dependent on the electrode contact impedance, which is determined by the characteristics of the recording electrode (geometry, position, material). These properties are essential for the observability of HFOs. In this study, a previously published hippocampal neural network model is used for the simulation of interictal HFOs. An additional microelectrode model layer is implemented in order to simulate the impact of using different types and characteristics of microelectrodes on the recorded HFOs. Results indicate that a small layer PEDOT/PSS and PEDOT/CNT on microelectrodes can effectively decrease their impedance resulting in the increase of HFOs observability. This model-based study can lead to the actual design of new electrodes that will ultimately contribute to improved diagnosis prior to invasive therapies.