This thesis investigates whether an EEG headset can be used to distinguish motor imagery signals in real time for a Brain Computer Interface (BCI).The specific EEG headset used for this project is the gtec Unicorn Hybrid Black. The aim of this subgroup is to stream the data in real time, preprocess the data, and create a training dataset using recordings from subjects. The sample from the EEG headset is streamed to a laptop using a live data streaming framework called Labstreaminglayer. The data is then filtered using frequency filters and ICA to remove noise and artifacts. Finally, ERDS plots are used to check the signal quality of the recordings. Recordings of sufficient quality should have (de)synchronisation peaks after the prompt is displayed. Before the recordings are made, an experimental setup is set up. This includes prompts with four different movements that the subject is asked to imagine. The data is sent to a GUI to be visualized with various graphs. It is also sent to a machine learning model to classify the movements. It was concluded that the subsystem could successfully stream data from the cap, process the data, and verify the quality of the data. Using ERDS plots, it was verified that some, but not all, MI actions are distinguishable for one individual. However, further verification of other individuals is required to conclude whether this is a systematic problem or is due to the individual.