  Brain-Computer Interfaces (BCI) have allowed for direct communication from
the brain to external applications for the automatic detection of cognitive
processes such as error recognition. Error-related potentials (ErrPs) are a
particular brain signal elicited when one commits or observes an erroneous
event. However, due to the noisy properties of the brain and recording devices,
ErrPs vary from instance to instance as they are combined with an assortment of
other brain signals, biological noise, and external noise, making the
classification of ErrPs a non-trivial problem. Recent works have revealed
particular cognitive processes such as awareness, embodiment, and
predictability that contribute to ErrP variations. In this paper, we explore
the performance of classifier transferability when trained on different ErrP
variation datasets generated by varying the levels of awareness and embodiment
for a given task. In particular, we look at transference between observational
and interactive ErrP categories when elicited by similar and differing tasks.
Our empirical results provide an exploratory analysis into the ErrP
transferability problem from a data perspective.
