  Evaluating human-computer interaction is essential as a broadening population
uses machines, sometimes in sensitive contexts. However, traditional evaluation
methods may fail to combine real-time measures, an "objective" approach and
data contextualization. In this review we look at how adding neuroimaging
techniques can respond to such needs. We focus on electroencephalography (EEG),
as it could be handled effectively during a dedicated evaluation phase. We
identify workload, attention, vigilance, fatigue, error recognition, emotions,
engagement, flow and immersion as being recognizable by EEG. We find that
workload, attention and emotions assessments would benefit the most from EEG.
Moreover, we advocate to study further error recognition through neuroimaging
to enhance usability and increase user experience.
