EEG (Electroencephalography) cap preparation requires a significant amount of time before actual measurements can be performed. It takes even longer for EEG caps that need some form of conductive gel or saltwater to decrease contact impedance between the electrodes and the scalp. In this thesis, several methods for detecting electrodes in a camera image and identifying detected electrodes are developed and evaluated for performance and reliability. Additionally, an application is developed that leverages augmented reality to visualize important values, like the impedance of electrodes, directly in the view of the operator. The proposed application runs on the Microsoft Hololens 2 that is used by the operator to overlay a visualization of impedance values of the electrodes over the electrodes themselves, which lets the operator see those values immediately. This reduces the time required to look at a separate device that shows impedance values and locate electrodes on the cap that require additional preparation. However, the identification of electrodes from the camera image does not work reliably enough with the proposed methods, and several ArUco markers, placed in predefined locations on the EEG cap, are used for aligning the virtual representation of the electrodes with the real electrodes.