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

Michael T. Knierim (), Max Schemmer () and Monica Perusquía-Hernández ()
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Michael T. Knierim: Karlsruhe Institute of Technology, Institute for Information Systems and Marketing
Max Schemmer: Karlsruhe Institute of Technology, Institute for Information Systems and Marketing
Monica Perusquía-Hernández: University of Essex

A chapter in Information Systems and Neuroscience, 2021, pp 47-55 from Springer

Abstract: Abstract 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.

Keywords: Face activity; Distal EMG; cEEGrid; OpenBCI; Random forest (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-88900-5_6

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