Functional Near infrared Spectroscopy (fNIRS) is a relatively young modality for measuring brain activity which has recently shown promising results for building Brain Computer Interfaces (BCI). Due to its infancy, there are still no standard approaches for meaningful features and classifiers for single trial analysis of fNIRS. Most studies are limited to established classifiers from EEG-based BCIs and very simple features. The feasibility of more complex and powerful classification approaches like Deep Neural Networks has, to the best of our knowledge, not been investigated for fNIRS based BCI. These networks have recently become increasingly popular, as they outperformed conventional machine learning methods for a variety of tasks, due in part to advances in training methods for neural networks. In this paper, we show how Deep Neural Networks can be used to classify brain activation patterns measured by fNIRS and compare them with previously used methods.