Review of the Use of Electroencephalography as an Evaluation Method for Human-Computer Interaction
This work addresses the need for improved evaluation methods in human-computer interaction for users in sensitive contexts, but it is incremental as it reviews existing EEG applications without introducing new techniques.
This review examines the use of electroencephalography (EEG) as a method to evaluate human-computer interaction, identifying that EEG can recognize factors like workload, attention, and emotions, with these assessments potentially benefiting the most from its application.
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.