  The increasing quality and affordability of consumer electroencephalogram
(EEG) headsets make them attractive for situations where medical grade devices
are impractical. Predicting and tracking cognitive states is possible for tasks
that were previously not conducive to EEG monitoring. For instance, monitoring
operators for states inappropriate to the task (e.g. drowsy drivers), tracking
mental health (e.g. anxiety) and productivity (e.g. tiredness) are among
possible applications for the technology. Consumer grade EEG headsets are
affordable and relatively easy to use, but they lack the resolution and quality
of signal that can be achieved using medical grade EEG devices. Thus, the key
questions remain: to what extent are wearable EEG devices capable of mental
state recognition, and what kind of mental states can be accurately recognized
with these devices? In this work, we examined responses to two different types
of input: instructional (logical) versus recreational (emotional) videos, using
a range of machine-learning methods. We tried SVMs, sparse logistic regression,
and Deep Belief Networks, to discriminate between the states of mind induced by
different types of video input, that can be roughly labeled as logical vs.
emotional. Our results demonstrate a significant potential of wearable EEG
devices in differentiating cognitive states between situations with large
contextual but subtle apparent differences.
