In brain-computer interface (BCI) research, electroencephalograms (EEGs) such as the Unicorn Hybrid Black (UHB) have entered the market as low-cost alternatives to other EEG devices. This study has two aims: the first is to assess the suitability of the UHB for BCI research, and the second is to assess the feasibility of a meditation BCI designed to provide users with feedback about mind wandering episodes. A BCI was created using the UHB and corresponding Python API to assess various machine learning algorithms’ classification accuracy of a meditation paradigm that uses self-caught experience sampling to capture mind wandering. Key findings suggest that while the UHB is sufficient to capture relevant brain signals associated with mind wandering, more research is required on appropriate intervention techniques.