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Distributed Remote EEG Data Collection for NeuroIS Research: A Methodological Framework

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Augmented Cognition (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12776))

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Abstract

Remote electroencephalography (EEG) studies offers the exciting opportunity to gather data within a participants’ home environment. However, remote EEG data collection trades some internal validity for ecological validity. When interacting with interfaces or other artifacts in remote settings, neurophysiological responses and behaviour may display distinct differences compared to laboratory studies. We propose a methodological approach composed of several recommendations and an iterative process framework to support this new avenue of research. The framework was developed during workshops composed of a diverse panel and a literature review of relevant research to complement our discoveries. We highlight and discuss the significant challenges associated with remote EEG data collection, and propose recommendations. We introduce the concept of self-applicability and propose a set of measures to guarantee good signal quality. Additionally, we offer specific recommendations for research design, training, and data collection strategies. We offer the iterative process framework to provide support rigorous data collection, innovative research questions, and the construction of large-scale datasets from remote EEG studies.

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Notes

  1. 1.

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Authors and Affiliations

  1. HEC Montréal, Montréal, QC, Canada

    Théophile Demazure, Alexander J. Karran, Jared Boasen, Pierre-Majorique Léger & Sylvain Sénécal

  2. Hokkaido University, Sapporo, Hokkaido, Japan

    Jared Boasen

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  1. Théophile Demazure
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  2. Alexander J. Karran
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  4. Pierre-Majorique Léger
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  5. Sylvain Sénécal
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  1. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  2. Design Interactive, Inc., Orlando, FL, USA

    Cali M. Fidopiastis

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Demazure, T., Karran, A.J., Boasen, J., Léger, PM., Sénécal, S. (2021). Distributed Remote EEG Data Collection for NeuroIS Research: A Methodological Framework. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2021. Lecture Notes in Computer Science(), vol 12776. Springer, Cham. https://doi.org/10.1007/978-3-030-78114-9_1

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