There is a lot to be gained by using topological data analysis (TDA) in conjunction with domain knowledge. As an example, consider the task where one wants to cluster brain states based on the underlying neural activity. Electroencephalograms (EEGs) are a common tool used to investigate neural activity by detecting electrical signals through sensors affixed to the scalp. EEG is relatively easy to use and provides high temporal resolution. However, it has low spatial resolution and prone to contamination with artifacts of movement or signals from external sources. Thus, for tasks like clustering brain states, it is difficult to capture the underlying structure and connectivity of individual states from EEG data. TDA, specifically the Mapper algorithm, has been used successfully in these types of problem spaces to pull important and relevant information from datasets. But, when applied directly to EEG data, Mapper does not reveal any structure or information. Luckily, there is a plethora of research and tools that have been developed to process and examine EEG data. Specifically, researchers have found that looking at the signal in the frequency domain can often provide insight into the neural activity. As such, we use the power spectral density paired with Mapper to create MapperEEG (MEEG). MEEG is neuroscience-infused topological tool that can cluster brain states without any pre-labeling or prior knowledge. In this talk, we will illustrate the importance of using prior domain knowledge within the EEG context, introduce the MEEG algorithm as an example of combining domain knowledge and TDA, and demonstrate its use on clustering brain state during a teaming task.