Exploring the Recognition of Facial Activities Through Around-the-Ear Electrode Arrays (cEEGrids)

NeuroIS scholars increasingly rely on more extensive and diverse
sensor data to improve the understanding of information system (IS) use and to
develop adaptive IS that foster individual and organizational productivity,
growth, and well-being. Collecting such data often requires multiple recording
devices, which leads to inflated study cost and decreased external validity due
to greater intrusion in natural behavior. To overcome this problem, we investigated
the potential of using an around-the-ear electrode array capable of capturing
neural and cardiac activity for detecting an additional set of variables,
namely facial muscle activity. We find that reading, speaking, chewing, jaw
clenching, and six posed emotion expressions can be differentiated well by a
Random Forest classifier. The results are complemented by the presentation of
an open-source signal acquisition system. Thereby, an economical approach for
naturalistic NeuroIS research and artefact development is provided.