Brain-Computer Interfaces and especially passive Brain-Computer interfaces (pBCI), with their ability to estimate and monitor user mental states, are receiving increasing attention from both the fundamental research and the applied research and development communities. Testing new pipelines and benchmarking classifiers and feature extraction algorithms is central to further research within this domain. Unfortunately, data sharing in pBCI research is still scarce. The COG-BCI database encompasses the recordings of 29 participants over 3 separate sessions with 4 different tasks (MATB, N-Back, PVT, Flanker) designed to elicit different mental states, for a total of over 100 hours of open EEG data. This dataset was validated on a subjective, behavioral and physiological level, to ensure its usefulness to the pBCI community. Furthermore, a proof of concept is given with an example of mental workload estimation pipeline and results, to ensure that the data can be used for the design and evaluation of pBCI pipelines. This body of work presents a large effort to promote the use of pBCIs in an open science framework.
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